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Zheyuan Wu
2026-03-11 16:01:12 -05:00
parent 1944fa612a
commit 254eec3be5
49 changed files with 3866 additions and 3915 deletions
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latex/.gitignore → .gitignore vendored
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codes/README.md
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# Simulation
## Define random sampling using standard uniform measure on the unit sphere
## Define and visualized the concentration of measure phenomenon on complex projective space
## Define random sampling using Majorana Stellar representation
# Simulation
## Define random sampling using standard uniform measure on the unit sphere
## Define and visualized the concentration of measure phenomenon on complex projective space
## Define random sampling using Majorana Stellar representation

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codes/experiment_v0.1.py
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codes/experiment_v0.2/config.py
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"""Edit globals here; no CLI parser is used."""
from datetime import datetime
from pathlib import Path
SEED = 7
KAPPA = 1e-3
NUM_SAMPLES = 10**4 # requested default
LIPSCHITZ_PAIRS = 12_000
LIPSCHITZ_RESERVOIR = 4_096
MAJORANA_STAR_STATES = 16 # only for visualization
MAX_STAR_DEGREE = 63 # avoid unstable huge root-finding plots
BACKEND = "auto" # auto | jax | numpy
JAX_PLATFORM = "" # "", "cpu", "gpu"; set before importing JAX
RESULTS_DIR = Path("./results") / f"exp-{datetime.now():%Y%m%d-%H%M%S}"
# Chosen so the three families have comparable intrinsic dimensions:
# sphere S^(m-1), CP^(d_A d_B - 1), and Sym^N(C^2) ~ CP^N.
SPHERE_DIMS = [16, 64, 256, 1024]
CP_DIMS = [(4, 4), (8, 8), (16, 16), (32, 32)]
MAJORANA_N = [15, 63, 255, 1023]
# Batch sizes are the main speed knob; reduce CP batches first if memory is tight.
BATCH = {"sphere": 32_768, "cp": 256, "majorana": 65_536}
"""Edit globals here; no CLI parser is used."""
from datetime import datetime
from pathlib import Path
SEED = 114514
KAPPA = 1e-3
NUM_SAMPLES = 10**6 # requested default
LIPSCHITZ_PAIRS = 12_000
LIPSCHITZ_RESERVOIR = 4_096
MAJORANA_STAR_STATES = 16 # only for visualization
MAX_STAR_DEGREE = 63 # avoid unstable huge root-finding plots
BACKEND = "auto" # auto | jax | numpy
JAX_PLATFORM = "gpu" # "", "cpu", "gpu"; set before importing JAX
RESULTS_DIR = (
Path.joinpath(Path.cwd(), Path("./results")) / f"exp-{datetime.now():%Y%m%d-%H%M%S}"
)
# Chosen so the three families have comparable intrinsic dimensions:
# sphere S^(m-1), CP^(d_A d_B - 1), and Sym^N(C^2) ~ CP^N.
SPHERE_DIMS = [1<<i for i in range(4, 12)]
CP_DIMS = [(1<<i, 1<<i) for i in range(4, 12)]
MAJORANA_N = [(1<<i)-1 for i in range(4, 12)]
# Batch sizes are the main speed knob; reduce CP batches first if memory is tight.
BATCH = {"sphere": 32_768, "cp": 256, "majorana": 65_536}

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codes/experiment_v0.2/main.py
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@@ -1,85 +1,112 @@
#!/usr/bin/env python3
"""Unified Monte Carlo for S^(m-1), CP^n, and symmetric-state CP^N via Majorana stars."""
from __future__ import annotations
import os
from pathlib import Path
import numpy as np
import config
if config.JAX_PLATFORM:
os.environ["JAX_PLATFORM_NAME"] = config.JAX_PLATFORM
from sampling_pipeline import ( # noqa: E402
plot_cross_space_comparison,
plot_family_summary,
plot_histogram,
plot_majorana_stars,
plot_tail,
simulate_space,
write_summary_csv,
)
from spaces import ComplexProjectiveSpace, MajoranaSymmetricSpace, UnitSphereSpace # noqa: E402
def main() -> None:
outdir = Path(config.RESULTS_DIR)
outdir.mkdir(parents=True, exist_ok=True)
spaces = (
[UnitSphereSpace(m) for m in config.SPHERE_DIMS]
+ [ComplexProjectiveSpace(a, b) for a, b in config.CP_DIMS]
+ [MajoranaSymmetricSpace(n) for n in config.MAJORANA_N]
)
seeds = np.random.SeedSequence(config.SEED).spawn(len(spaces) + 16)
results = []
for i, space in enumerate(spaces):
result = simulate_space(
space,
num_samples=config.NUM_SAMPLES,
batch=config.BATCH[space.family],
kappa=config.KAPPA,
seed=int(seeds[i].generate_state(1, dtype=np.uint32)[0]),
backend=config.BACKEND,
lipschitz_pairs=config.LIPSCHITZ_PAIRS,
lipschitz_reservoir=config.LIPSCHITZ_RESERVOIR,
)
results.append(result)
plot_histogram(result, outdir)
plot_tail(result, space, outdir)
if space.family == "majorana" and space.N <= config.MAX_STAR_DEGREE:
star_seed = int(seeds[len(spaces) + i].generate_state(1, dtype=np.uint32)[0])
from pipeline import _sample_stream # local import to avoid exporting internals
states, _ = _sample_stream(space, config.MAJORANA_STAR_STATES, min(config.MAJORANA_STAR_STATES, config.BATCH["majorana"]), star_seed, config.BACKEND, keep_states=True)
plot_majorana_stars(space, states, outdir)
results.sort(key=lambda r: (r.family, r.intrinsic_dim))
write_summary_csv(results, outdir / "observable_diameter_summary.csv")
for fam in ("sphere", "cp", "majorana"):
plot_family_summary(results, fam, outdir)
plot_cross_space_comparison(results, outdir)
with (outdir / "run_config.txt").open("w") as fh:
fh.write(
f"SEED={config.SEED}\nKAPPA={config.KAPPA}\nNUM_SAMPLES={config.NUM_SAMPLES}\n"
f"LIPSCHITZ_PAIRS={config.LIPSCHITZ_PAIRS}\nLIPSCHITZ_RESERVOIR={config.LIPSCHITZ_RESERVOIR}\n"
f"BACKEND={config.BACKEND}\nJAX_PLATFORM={config.JAX_PLATFORM}\n"
f"SPHERE_DIMS={config.SPHERE_DIMS}\nCP_DIMS={config.CP_DIMS}\nMAJORANA_N={config.MAJORANA_N}\n"
f"BATCH={config.BATCH}\n"
)
print("family dim mean(bits) part_diam(bits) norm_proxy_q99")
for r in results:
q = f"{r.normalized_proxy_q99:.6g}" if r.normalized_proxy_q99 == r.normalized_proxy_q99 else "nan"
print(f"{r.family:8s} {r.intrinsic_dim:5d} {r.mean:11.6f} {r.partial_diameter:16.6f} {q:>14s}")
print(f"\nWrote results to: {outdir.resolve()}")
if __name__ == "__main__":
main()
#!/usr/bin/env python3
"""Unified Monte Carlo for S^(m-1), CP^n, and symmetric-state CP^N via Majorana stars."""
from __future__ import annotations
import os
from pathlib import Path
import numpy as np
import sys
# Add the parent directory to sys.path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import config
if config.JAX_PLATFORM:
os.environ["JAX_PLATFORM_NAME"] = config.JAX_PLATFORM
from sampling_pipeline import (
plot_cross_space_comparison,
plot_family_summary,
plot_histogram,
plot_majorana_stars,
plot_tail,
simulate_space,
write_summary_csv,
)
from spaces import (
ComplexProjectiveSpace,
MajoranaSymmetricSpace,
UnitSphereSpace,
)
def main() -> None:
outdir = Path(config.RESULTS_DIR)
outdir.mkdir(parents=True, exist_ok=True)
spaces = (
[UnitSphereSpace(m) for m in config.SPHERE_DIMS]
+ [ComplexProjectiveSpace(a, b) for a, b in config.CP_DIMS]
+ [MajoranaSymmetricSpace(n) for n in config.MAJORANA_N]
)
seeds = np.random.SeedSequence(config.SEED).spawn(len(spaces) + 16)
results = []
for i, space in enumerate(spaces):
result = simulate_space(
space,
num_samples=config.NUM_SAMPLES,
batch=config.BATCH[space.family],
kappa=config.KAPPA,
seed=int(seeds[i].generate_state(1, dtype=np.uint32)[0]),
backend=config.BACKEND,
lipschitz_pairs=config.LIPSCHITZ_PAIRS,
lipschitz_reservoir=config.LIPSCHITZ_RESERVOIR,
)
results.append(result)
plot_histogram(result, outdir)
plot_tail(result, space, outdir)
if space.family == "majorana" and space.N <= config.MAX_STAR_DEGREE:
star_seed = int(
seeds[len(spaces) + i].generate_state(1, dtype=np.uint32)[0]
)
from sampling_pipeline import (
_sample_stream,
) # local import to avoid exporting internals
states, _ = _sample_stream(
space,
config.MAJORANA_STAR_STATES,
min(config.MAJORANA_STAR_STATES, config.BATCH["majorana"]),
star_seed,
config.BACKEND,
keep_states=True,
)
plot_majorana_stars(space, states, outdir)
results.sort(key=lambda r: (r.family, r.intrinsic_dim))
write_summary_csv(results, outdir / "observable_diameter_summary.csv")
for fam in ("sphere", "cp", "majorana"):
plot_family_summary(results, fam, outdir)
plot_cross_space_comparison(results, outdir)
with (outdir / "run_config.txt").open("w") as fh:
fh.write(
f"SEED={config.SEED}\nKAPPA={config.KAPPA}\nNUM_SAMPLES={config.NUM_SAMPLES}\n"
f"LIPSCHITZ_PAIRS={config.LIPSCHITZ_PAIRS}\nLIPSCHITZ_RESERVOIR={config.LIPSCHITZ_RESERVOIR}\n"
f"BACKEND={config.BACKEND}\nJAX_PLATFORM={config.JAX_PLATFORM}\n"
f"SPHERE_DIMS={config.SPHERE_DIMS}\nCP_DIMS={config.CP_DIMS}\nMAJORANA_N={config.MAJORANA_N}\n"
f"BATCH={config.BATCH}\n"
)
print("family dim mean(bits) part_diam(bits) norm_proxy_q99")
for r in results:
q = (
f"{r.normalized_proxy_q99:.6g}"
if r.normalized_proxy_q99 == r.normalized_proxy_q99
else "nan"
)
print(
f"{r.family:8s} {r.intrinsic_dim:5d} {r.mean:11.6f} {r.partial_diameter:16.6f} {q:>14s}"
)
print(f"\nWrote results to: {outdir.resolve()}")
if __name__ == "__main__":
main()

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codes/experiment_v0.2/requirements.txt
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numpy>=1.26
matplotlib>=3.8
tqdm>=4.66
# CPU-only JAX
# jax
# Apple Metal JAX (experimental; complex64/complex128 currently unsupported)
# jax-metal
# NVIDIA Linux JAX
jax[cuda13]
# or, if needed:
numpy>=1.26
matplotlib>=3.8
tqdm>=4.66
# CPU-only JAX
# jax
# Apple Metal JAX (experimental; complex64/complex128 currently unsupported)
# jax-metal
# NVIDIA Linux JAX
jax[cuda13]
# or, if needed:
# jax[cuda12]

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codes/experiment_v0.2/sampling_pipline.py
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from __future__ import annotations
import csv
import math
from dataclasses import dataclass, field
from pathlib import Path
from typing import Sequence
import matplotlib.pyplot as plt
import numpy as np
from tqdm.auto import tqdm
from spaces import HAS_JAX, MetricMeasureSpace, jax, random
@dataclass
class SystemResult:
"""Compact record of one simulated metric-measure system."""
family: str
label: str
slug: str
intrinsic_dim: int
num_samples: int
kappa: float
mass: float
observable_max: float
values: np.ndarray
partial_diameter: float
interval_left: float
interval_right: float
mean: float
median: float
std: float
empirical_lipschitz_max: float
empirical_lipschitz_q99: float
normalized_proxy_max: float
normalized_proxy_q99: float
theory: dict[str, float] = field(default_factory=dict)
def partial_diameter(samples: np.ndarray, mass: float) -> tuple[float, float, float]:
"""Shortest interval carrying the requested empirical mass."""
x = np.sort(np.asarray(samples, float))
n = len(x)
if n == 0 or not (0.0 < mass <= 1.0):
raise ValueError("Need nonempty samples and mass in (0,1].")
if n == 1:
return 0.0, float(x[0]), float(x[0])
m = max(1, int(math.ceil(mass * n)))
if m <= 1:
return 0.0, float(x[0]), float(x[0])
w = x[m - 1 :] - x[: n - m + 1]
i = int(np.argmin(w))
return float(w[i]), float(x[i]), float(x[i + m - 1])
def empirical_lipschitz(
space: MetricMeasureSpace,
states: np.ndarray,
values: np.ndarray,
rng: np.random.Generator,
num_pairs: int,
) -> tuple[float, float]:
"""Estimate max and q99 slope over random state pairs."""
n = len(states)
if n < 2 or num_pairs <= 0:
return float("nan"), float("nan")
i = rng.integers(0, n, size=num_pairs)
j = rng.integers(0, n - 1, size=num_pairs)
j += (j >= i)
d = space.metric_pairs(states[i], states[j])
good = d > 1e-12
if not np.any(good):
return float("nan"), float("nan")
r = np.abs(values[i] - values[j])[good] / d[good]
return float(np.max(r)), float(np.quantile(r, 0.99))
def _sample_stream(
space: MetricMeasureSpace,
n: int,
batch: int,
seed: int,
backend: str,
keep_states: bool,
) -> tuple[np.ndarray | None, np.ndarray]:
"""Sample values, optionally keeping state vectors for Lipschitz estimation."""
vals = np.empty(n, dtype=np.float32)
states = np.empty((n, space.state_dim), dtype=np.float32 if space.family == "sphere" else np.complex64) if keep_states else None
use_jax = backend != "numpy" and HAS_JAX
desc = f"{space.slug}: {n:,} samples"
if use_jax:
key = random.PRNGKey(seed)
for s in tqdm(range(0, n, batch), desc=desc, unit="batch"):
b = min(batch, n - s)
key, sub = random.split(key)
x, y = space.sample_jax(sub, b)
vals[s : s + b] = np.asarray(jax.device_get(y), dtype=np.float32)
if keep_states:
states[s : s + b] = np.asarray(jax.device_get(x), dtype=states.dtype)
else:
rng = np.random.default_rng(seed)
for s in tqdm(range(0, n, batch), desc=desc, unit="batch"):
b = min(batch, n - s)
x, y = space.sample_np(rng, b)
vals[s : s + b] = y
if keep_states:
states[s : s + b] = x.astype(states.dtype)
return states, vals
def simulate_space(
space: MetricMeasureSpace,
*,
num_samples: int,
batch: int,
kappa: float,
seed: int,
backend: str,
lipschitz_pairs: int,
lipschitz_reservoir: int,
) -> SystemResult:
"""Main Monte Carlo pass plus a smaller Lipschitz pass."""
vals = _sample_stream(space, num_samples, batch, seed, backend, keep_states=False)[1]
mass = 1.0 - kappa
width, left, right = partial_diameter(vals, mass)
r_states, r_vals = _sample_stream(space, min(lipschitz_reservoir, num_samples), min(batch, lipschitz_reservoir), seed + 1, backend, keep_states=True)
lip_rng = np.random.default_rng(seed + 2)
lip_max, lip_q99 = empirical_lipschitz(space, r_states, r_vals, lip_rng, lipschitz_pairs)
nmax = width / lip_max if lip_max == lip_max and lip_max > 0 else float("nan")
nq99 = width / lip_q99 if lip_q99 == lip_q99 and lip_q99 > 0 else float("nan")
return SystemResult(
family=space.family,
label=space.label,
slug=space.slug,
intrinsic_dim=space.intrinsic_dim,
num_samples=num_samples,
kappa=kappa,
mass=mass,
observable_max=space.observable_max,
values=vals,
partial_diameter=width,
interval_left=left,
interval_right=right,
mean=float(np.mean(vals)),
median=float(np.median(vals)),
std=float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0,
empirical_lipschitz_max=lip_max,
empirical_lipschitz_q99=lip_q99,
normalized_proxy_max=nmax,
normalized_proxy_q99=nq99,
theory=space.theory(kappa),
)
def write_summary_csv(results: Sequence[SystemResult], out_path: Path) -> None:
"""Write one flat CSV with optional theory fields."""
extras = sorted({k for r in results for k in r.theory})
fields = [
"family", "label", "intrinsic_dim", "num_samples", "kappa", "mass",
"observable_max_bits", "partial_diameter_bits", "interval_left_bits", "interval_right_bits",
"mean_bits", "median_bits", "std_bits", "empirical_lipschitz_max", "empirical_lipschitz_q99",
"normalized_proxy_max", "normalized_proxy_q99",
] + extras
with out_path.open("w", newline="") as fh:
w = csv.DictWriter(fh, fieldnames=fields)
w.writeheader()
for r in results:
row = {
"family": r.family,
"label": r.label,
"intrinsic_dim": r.intrinsic_dim,
"num_samples": r.num_samples,
"kappa": r.kappa,
"mass": r.mass,
"observable_max_bits": r.observable_max,
"partial_diameter_bits": r.partial_diameter,
"interval_left_bits": r.interval_left,
"interval_right_bits": r.interval_right,
"mean_bits": r.mean,
"median_bits": r.median,
"std_bits": r.std,
"empirical_lipschitz_max": r.empirical_lipschitz_max,
"empirical_lipschitz_q99": r.empirical_lipschitz_q99,
"normalized_proxy_max": r.normalized_proxy_max,
"normalized_proxy_q99": r.normalized_proxy_q99,
}
row.update(r.theory)
w.writerow(row)
def plot_histogram(r: SystemResult, outdir: Path) -> None:
"""Per-system histogram with interval and theory overlays when available."""
v = r.values
vmin, vmax = float(np.min(v)), float(np.max(v))
vr = max(vmax - vmin, 1e-9)
plt.figure(figsize=(8.5, 5.5))
plt.hist(v, bins=48, density=True, alpha=0.75)
plt.axvspan(r.interval_left, r.interval_right, alpha=0.18, label=f"shortest {(r.mass):.0%} interval")
plt.axvline(r.observable_max, linestyle="--", linewidth=2, label="observable upper bound")
plt.axvline(r.mean, linestyle="-.", linewidth=2, label="empirical mean")
if "page_average_bits" in r.theory:
plt.axvline(r.theory["page_average_bits"], linestyle=":", linewidth=2, label="Page average")
if "hayden_cutoff_bits" in r.theory:
plt.axvline(r.theory["hayden_cutoff_bits"], linewidth=2, label="Hayden cutoff")
plt.xlim(vmin - 0.1 * vr, vmax + 0.25 * vr)
plt.xlabel("Entropy observable (bits)")
plt.ylabel("Empirical density")
plt.title(r.label)
plt.legend(frameon=False)
plt.tight_layout()
plt.savefig(outdir / f"hist_{r.slug}.png", dpi=180)
plt.close()
def plot_tail(r: SystemResult, space: MetricMeasureSpace, outdir: Path) -> None:
"""Upper-tail plot for the entropy deficit from its natural ceiling."""
deficits = r.observable_max - np.sort(r.values)
n = len(deficits)
ccdf = np.maximum(1.0 - (np.arange(1, n + 1) / n), 1.0 / n)
x = np.linspace(0.0, max(float(np.max(deficits)), 1e-6), 256)
plt.figure(figsize=(8.5, 5.5))
plt.semilogy(deficits, ccdf, marker="o", linestyle="none", markersize=3, alpha=0.45, label="empirical tail")
bound = space.tail_bound(x)
if bound is not None:
plt.semilogy(x, bound, linewidth=2, label="theory bound")
plt.xlabel("Entropy deficit (bits)")
plt.ylabel("Tail probability")
plt.title(f"Tail plot: {r.label}")
plt.legend(frameon=False)
plt.tight_layout()
plt.savefig(outdir / f"tail_{r.slug}.png", dpi=180)
plt.close()
def plot_family_summary(results: Sequence[SystemResult], family: str, outdir: Path) -> None:
"""Original-style summary plots, one family at a time."""
rs = sorted([r for r in results if r.family == family], key=lambda z: z.intrinsic_dim)
if not rs:
return
x = np.array([r.intrinsic_dim for r in rs], float)
pd = np.array([r.partial_diameter for r in rs], float)
sd = np.array([r.std for r in rs], float)
md = np.array([r.observable_max - r.mean for r in rs], float)
plt.figure(figsize=(8.5, 5.5))
plt.plot(x, pd, marker="o", linewidth=2, label=r"shortest $(1-\kappa)$ interval")
plt.plot(x, sd, marker="s", linewidth=2, label="empirical std")
plt.plot(x, md, marker="^", linewidth=2, label="mean deficit")
plt.xlabel("Intrinsic dimension")
plt.ylabel("Bits")
plt.title(f"Concentration summary: {family}")
plt.legend(frameon=False)
plt.tight_layout()
plt.savefig(outdir / f"summary_{family}.png", dpi=180)
plt.close()
good = [r for r in rs if r.normalized_proxy_q99 == r.normalized_proxy_q99]
if good:
x = np.array([r.intrinsic_dim for r in good], float)
y1 = np.array([r.normalized_proxy_max for r in good], float)
y2 = np.array([r.normalized_proxy_q99 for r in good], float)
plt.figure(figsize=(8.5, 5.5))
plt.plot(x, y1, marker="o", linewidth=2, label="width / Lipschitz max")
plt.plot(x, y2, marker="s", linewidth=2, label="width / Lipschitz q99")
plt.xlabel("Intrinsic dimension")
plt.ylabel("Normalized proxy")
plt.title(f"Lipschitz-normalized proxy: {family}")
plt.legend(frameon=False)
plt.tight_layout()
plt.savefig(outdir / f"normalized_{family}.png", dpi=180)
plt.close()
def plot_cross_space_comparison(results: Sequence[SystemResult], outdir: Path) -> None:
"""Direct comparison of the three spaces on one figure."""
marks = {"sphere": "o", "cp": "s", "majorana": "^"}
plt.figure(figsize=(8.8, 5.6))
for fam in ("sphere", "cp", "majorana"):
rs = sorted([r for r in results if r.family == fam], key=lambda z: z.intrinsic_dim)
if rs:
plt.plot([r.intrinsic_dim for r in rs], [r.partial_diameter for r in rs], marker=marks[fam], linewidth=2, label=fam)
plt.xlabel("Intrinsic dimension")
plt.ylabel("Partial diameter in bits")
plt.title("Entropy-based observable-diameter proxy: raw width comparison")
plt.legend(frameon=False)
plt.tight_layout()
plt.savefig(outdir / "compare_partial_diameter.png", dpi=180)
plt.close()
plt.figure(figsize=(8.8, 5.6))
for fam in ("sphere", "cp", "majorana"):
rs = sorted([r for r in results if r.family == fam and r.normalized_proxy_q99 == r.normalized_proxy_q99], key=lambda z: z.intrinsic_dim)
if rs:
plt.plot([r.intrinsic_dim for r in rs], [r.normalized_proxy_q99 for r in rs], marker=marks[fam], linewidth=2, label=fam)
plt.xlabel("Intrinsic dimension")
plt.ylabel("Normalized proxy")
plt.title("Entropy-based observable-diameter proxy: normalized comparison")
plt.legend(frameon=False)
plt.tight_layout()
plt.savefig(outdir / "compare_normalized_proxy.png", dpi=180)
plt.close()
def plot_majorana_stars(space: MetricMeasureSpace, states: np.ndarray, outdir: Path) -> None:
"""Scatter Majorana stars in longitude/latitude coordinates."""
if not hasattr(space, "majorana_stars") or len(states) == 0:
return
pts = np.vstack([space.majorana_stars(s) for s in states])
x, y, z = pts[:, 0], pts[:, 1], np.clip(pts[:, 2], -1.0, 1.0)
lon, lat = np.arctan2(y, x), np.arcsin(z)
plt.figure(figsize=(8.8, 4.6))
plt.scatter(lon, lat, s=10, alpha=0.35)
plt.xlim(-math.pi, math.pi)
plt.ylim(-math.pi / 2, math.pi / 2)
plt.xlabel("longitude")
plt.ylabel("latitude")
plt.title(f"Majorana stars: {space.label}")
plt.tight_layout()
plt.savefig(outdir / f"majorana_stars_{space.slug}.png", dpi=180)
plt.close()

622
codes/experiment_v0.2/spaces.py
View File

@@ -1,284 +1,338 @@
from __future__ import annotations
import math
from dataclasses import dataclass
from typing import Any
import numpy as np
try:
import jax
import jax.numpy as jnp
from jax import random
jax.config.update("jax_enable_x64", False)
HAS_JAX = True
except Exception: # pragma: no cover
jax = jnp = random = None
HAS_JAX = False
HAYDEN_C = 1.0 / (8.0 * math.pi**2)
def entropy_bits_from_probs(p: Any, xp: Any) -> Any:
"""Return Shannon/von-Neumann entropy of probabilities/eigenvalues in bits."""
p = xp.clip(xp.real(p), 1e-30, 1.0)
return -xp.sum(p * xp.log2(p), axis=-1)
def fs_metric_np(x: np.ndarray, y: np.ndarray) -> np.ndarray:
"""Fubini-Study distance for batches of normalized complex vectors."""
ov = np.abs(np.sum(np.conj(x) * y, axis=-1))
return np.arccos(np.clip(ov, 0.0, 1.0))
def sphere_metric_np(x: np.ndarray, y: np.ndarray) -> np.ndarray:
"""Geodesic distance on the real unit sphere."""
dot = np.sum(x * y, axis=-1)
return np.arccos(np.clip(dot, -1.0, 1.0))
class MetricMeasureSpace:
"""Minimal interface: direct sampler + metric + scalar observable ceiling."""
family: str = "base"
@property
def label(self) -> str:
raise NotImplementedError
@property
def slug(self) -> str:
raise NotImplementedError
@property
def intrinsic_dim(self) -> int:
raise NotImplementedError
@property
def state_dim(self) -> int:
raise NotImplementedError
@property
def observable_max(self) -> float:
raise NotImplementedError
def sample_np(self, rng: np.random.Generator, batch: int) -> tuple[np.ndarray, np.ndarray]:
raise NotImplementedError
def sample_jax(self, key: Any, batch: int) -> tuple[Any, Any]:
raise NotImplementedError
def metric_pairs(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
raise NotImplementedError
def theory(self, kappa: float) -> dict[str, float]:
return {}
def tail_bound(self, deficits: np.ndarray) -> np.ndarray | None:
return None
@dataclass
class UnitSphereSpace(MetricMeasureSpace):
"""Uniform measure on the real unit sphere S^(m-1), observable H(x_i^2)."""
dim: int
family: str = "sphere"
@property
def label(self) -> str:
return f"S^{self.dim - 1}"
@property
def slug(self) -> str:
return f"sphere_{self.dim}"
@property
def intrinsic_dim(self) -> int:
return self.dim - 1
@property
def state_dim(self) -> int:
return self.dim
@property
def observable_max(self) -> float:
return math.log2(self.dim)
def sample_np(self, rng: np.random.Generator, batch: int) -> tuple[np.ndarray, np.ndarray]:
x = rng.normal(size=(batch, self.dim)).astype(np.float32)
x /= np.linalg.norm(x, axis=1, keepdims=True)
return x, entropy_bits_from_probs(x * x, np).astype(np.float32)
def sample_jax(self, key: Any, batch: int) -> tuple[Any, Any]:
x = random.normal(key, (batch, self.dim), dtype=jnp.float32)
x /= jnp.linalg.norm(x, axis=1, keepdims=True)
return x, entropy_bits_from_probs(x * x, jnp)
def metric_pairs(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
return sphere_metric_np(x, y)
@dataclass
class ComplexProjectiveSpace(MetricMeasureSpace):
"""Haar-random pure states on C^(d_A d_B), observable = entanglement entropy."""
d_a: int
d_b: int
family: str = "cp"
def __post_init__(self) -> None:
if self.d_a <= 1 or self.d_b <= 1:
raise ValueError("Need d_A,d_B >= 2.")
if self.d_a > self.d_b:
self.d_a, self.d_b = self.d_b, self.d_a
@property
def label(self) -> str:
return f"CP^{self.d_a * self.d_b - 1} via C^{self.d_a}⊗C^{self.d_b}"
@property
def slug(self) -> str:
return f"cp_{self.d_a}x{self.d_b}"
@property
def intrinsic_dim(self) -> int:
return self.d_a * self.d_b - 1
@property
def state_dim(self) -> int:
return self.d_a * self.d_b
@property
def observable_max(self) -> float:
return math.log2(self.d_a)
def sample_np(self, rng: np.random.Generator, batch: int) -> tuple[np.ndarray, np.ndarray]:
g = (rng.normal(size=(batch, self.d_a, self.d_b)) + 1j * rng.normal(size=(batch, self.d_a, self.d_b)))
g = (g / math.sqrt(2.0)).astype(np.complex64)
g /= np.sqrt(np.sum(np.abs(g) ** 2, axis=(1, 2), keepdims=True))
rho = g @ np.swapaxes(np.conj(g), 1, 2)
lam = np.clip(np.linalg.eigvalsh(rho).real, 1e-30, 1.0)
return g.reshape(batch, -1), entropy_bits_from_probs(lam, np).astype(np.float32)
def sample_jax(self, key: Any, batch: int) -> tuple[Any, Any]:
k1, k2 = random.split(key)
g = (random.normal(k1, (batch, self.d_a, self.d_b), dtype=jnp.float32)
+ 1j * random.normal(k2, (batch, self.d_a, self.d_b), dtype=jnp.float32)) / math.sqrt(2.0)
g = g / jnp.sqrt(jnp.sum(jnp.abs(g) ** 2, axis=(1, 2), keepdims=True))
rho = g @ jnp.swapaxes(jnp.conj(g), -1, -2)
lam = jnp.clip(jnp.linalg.eigvalsh(rho).real, 1e-30, 1.0)
return g.reshape(batch, -1), entropy_bits_from_probs(lam, jnp)
def metric_pairs(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
return fs_metric_np(x, y)
def theory(self, kappa: float) -> dict[str, float]:
d = self.d_a * self.d_b
beta = self.d_a / (math.log(2.0) * self.d_b)
alpha = (math.log2(self.d_a) / math.sqrt(HAYDEN_C * (d - 1.0))) * math.sqrt(math.log(1.0 / kappa))
tail = sum(1.0 / k for k in range(self.d_b + 1, d + 1))
page = (tail - (self.d_a - 1.0) / (2.0 * self.d_b)) / math.log(2.0)
return {
"page_average_bits": page,
"hayden_mean_lower_bits": math.log2(self.d_a) - beta,
"hayden_cutoff_bits": math.log2(self.d_a) - (beta + alpha),
"hayden_one_sided_width_bits": beta + alpha,
"levy_scaling_width_bits": 2.0
* (math.log2(self.d_a) / math.sqrt(HAYDEN_C * (d - 1.0)))
* math.sqrt(math.log(2.0 / kappa)),
}
def tail_bound(self, deficits: np.ndarray) -> np.ndarray:
beta = self.d_a / (math.log(2.0) * self.d_b)
shifted = np.maximum(np.asarray(deficits, float) - beta, 0.0)
expo = -(self.d_a * self.d_b - 1.0) * HAYDEN_C * shifted**2 / (math.log2(self.d_a) ** 2)
out = np.exp(expo)
out[deficits <= beta] = 1.0
return np.clip(out, 0.0, 1.0)
@dataclass
class MajoranaSymmetricSpace(MetricMeasureSpace):
"""Haar-random symmetric N-qubit states; stars are for visualization only."""
N: int
family: str = "majorana"
@property
def label(self) -> str:
return f"Sym^{self.N}(C^2) ≅ CP^{self.N}"
@property
def slug(self) -> str:
return f"majorana_{self.N}"
@property
def intrinsic_dim(self) -> int:
return self.N
@property
def state_dim(self) -> int:
return self.N + 1
@property
def observable_max(self) -> float:
return 1.0 # one-qubit entropy upper bound
def _rho1_np(self, c: np.ndarray) -> np.ndarray:
k = np.arange(self.N + 1, dtype=np.float32)
p = np.abs(c) ** 2
rho11 = (p * k).sum(axis=1) / self.N
coef = np.sqrt((np.arange(self.N, dtype=np.float32) + 1.0) * (self.N - np.arange(self.N, dtype=np.float32))) / self.N
off = (np.conj(c[:, :-1]) * c[:, 1:] * coef).sum(axis=1)
rho = np.zeros((len(c), 2, 2), dtype=np.complex64)
rho[:, 0, 0] = 1.0 - rho11
rho[:, 1, 1] = rho11
rho[:, 0, 1] = off
rho[:, 1, 0] = np.conj(off)
return rho
def _rho1_jax(self, c: Any) -> Any:
k = jnp.arange(self.N + 1, dtype=jnp.float32)
p = jnp.abs(c) ** 2
rho11 = jnp.sum(p * k, axis=1) / self.N
kk = jnp.arange(self.N, dtype=jnp.float32)
coef = jnp.sqrt((kk + 1.0) * (self.N - kk)) / self.N
off = jnp.sum(jnp.conj(c[:, :-1]) * c[:, 1:] * coef, axis=1)
rho = jnp.zeros((c.shape[0], 2, 2), dtype=jnp.complex64)
rho = rho.at[:, 0, 0].set(1.0 - rho11)
rho = rho.at[:, 1, 1].set(rho11)
rho = rho.at[:, 0, 1].set(off)
rho = rho.at[:, 1, 0].set(jnp.conj(off))
return rho
def sample_np(self, rng: np.random.Generator, batch: int) -> tuple[np.ndarray, np.ndarray]:
c = (rng.normal(size=(batch, self.N + 1)) + 1j * rng.normal(size=(batch, self.N + 1)))
c = (c / math.sqrt(2.0)).astype(np.complex64)
c /= np.linalg.norm(c, axis=1, keepdims=True)
lam = np.clip(np.linalg.eigvalsh(self._rho1_np(c)).real, 1e-30, 1.0)
return c, entropy_bits_from_probs(lam, np).astype(np.float32)
def sample_jax(self, key: Any, batch: int) -> tuple[Any, Any]:
k1, k2 = random.split(key)
c = (random.normal(k1, (batch, self.N + 1), dtype=jnp.float32)
+ 1j * random.normal(k2, (batch, self.N + 1), dtype=jnp.float32)) / math.sqrt(2.0)
c = c / jnp.linalg.norm(c, axis=1, keepdims=True)
lam = jnp.clip(jnp.linalg.eigvalsh(self._rho1_jax(c)).real, 1e-30, 1.0)
return c, entropy_bits_from_probs(lam, jnp)
def metric_pairs(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
return fs_metric_np(x, y)
def majorana_stars(self, coeffs: np.ndarray) -> np.ndarray:
"""Map one symmetric state to its Majorana stars on S^2."""
a = np.array([((-1) ** k) * math.sqrt(math.comb(self.N, k)) * coeffs[k] for k in range(self.N + 1)], np.complex128)
poly = np.trim_zeros(a[::-1], trim="f")
roots = np.roots(poly) if len(poly) > 1 else np.empty(0, dtype=np.complex128)
r2 = np.abs(roots) ** 2
pts = np.c_[2 * roots.real / (1 + r2), 2 * roots.imag / (1 + r2), (r2 - 1) / (1 + r2)]
missing = self.N - len(pts)
if missing > 0:
pts = np.vstack([pts, np.tile(np.array([[0.0, 0.0, 1.0]]), (missing, 1))])
return pts.astype(np.float32)
from __future__ import annotations
import math
from dataclasses import dataclass
from typing import Any
import numpy as np
try:
import jax
import jax.numpy as jnp
from jax import random
jax.config.update("jax_enable_x64", False)
HAS_JAX = True
except Exception: # pragma: no cover
jax = jnp = random = None
HAS_JAX = False
HAYDEN_C = 1.0 / (8.0 * math.pi**2)
def entropy_bits_from_probs(p: Any, xp: Any) -> Any:
"""Return Shannon/von-Neumann entropy of probabilities/eigenvalues in bits."""
p = xp.clip(xp.real(p), 1e-30, 1.0)
return -xp.sum(p * xp.log2(p), axis=-1)
def fs_metric_np(x: np.ndarray, y: np.ndarray) -> np.ndarray:
"""Fubini-Study distance for batches of normalized complex vectors."""
ov = np.abs(np.sum(np.conj(x) * y, axis=-1))
return np.arccos(np.clip(ov, 0.0, 1.0))
def sphere_metric_np(x: np.ndarray, y: np.ndarray) -> np.ndarray:
"""Geodesic distance on the real unit sphere."""
dot = np.sum(x * y, axis=-1)
return np.arccos(np.clip(dot, -1.0, 1.0))
class MetricMeasureSpace:
"""Minimal interface: direct sampler + metric + scalar observable ceiling."""
family: str = "base"
@property
def label(self) -> str:
raise NotImplementedError
@property
def slug(self) -> str:
raise NotImplementedError
@property
def intrinsic_dim(self) -> int:
raise NotImplementedError
@property
def state_dim(self) -> int:
raise NotImplementedError
@property
def observable_max(self) -> float:
raise NotImplementedError
def sample_np(
self, rng: np.random.Generator, batch: int
) -> tuple[np.ndarray, np.ndarray]:
raise NotImplementedError
def sample_jax(self, key: Any, batch: int) -> tuple[Any, Any]:
raise NotImplementedError
def metric_pairs(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
raise NotImplementedError
def theory(self, kappa: float) -> dict[str, float]:
return {}
def tail_bound(self, deficits: np.ndarray) -> np.ndarray | None:
return None
@dataclass
class UnitSphereSpace(MetricMeasureSpace):
"""Uniform measure on the real unit sphere S^(m-1), observable H(x_i^2)."""
dim: int
family: str = "sphere"
@property
def label(self) -> str:
return f"S^{self.dim - 1}"
@property
def slug(self) -> str:
return f"sphere_{self.dim}"
@property
def intrinsic_dim(self) -> int:
return self.dim - 1
@property
def state_dim(self) -> int:
return self.dim
@property
def observable_max(self) -> float:
return math.log2(self.dim)
def sample_np(
self, rng: np.random.Generator, batch: int
) -> tuple[np.ndarray, np.ndarray]:
x = rng.normal(size=(batch, self.dim)).astype(np.float32)
x /= np.linalg.norm(x, axis=1, keepdims=True)
return x, entropy_bits_from_probs(x * x, np).astype(np.float32)
def sample_jax(self, key: Any, batch: int) -> tuple[Any, Any]:
x = random.normal(key, (batch, self.dim), dtype=jnp.float32)
x /= jnp.linalg.norm(x, axis=1, keepdims=True)
return x, entropy_bits_from_probs(x * x, jnp)
def metric_pairs(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
return sphere_metric_np(x, y)
@dataclass
class ComplexProjectiveSpace(MetricMeasureSpace):
"""Haar-random pure states on C^(d_A d_B), observable = entanglement entropy."""
d_a: int
d_b: int
family: str = "cp"
def __post_init__(self) -> None:
if self.d_a <= 1 or self.d_b <= 1:
raise ValueError("Need d_A,d_B >= 2.")
if self.d_a > self.d_b:
self.d_a, self.d_b = self.d_b, self.d_a
@property
def label(self) -> str:
return f"CP^{self.d_a * self.d_b - 1} via C^{self.d_a}⊗C^{self.d_b}"
@property
def slug(self) -> str:
return f"cp_{self.d_a}x{self.d_b}"
@property
def intrinsic_dim(self) -> int:
return self.d_a * self.d_b - 1
@property
def state_dim(self) -> int:
return self.d_a * self.d_b
@property
def observable_max(self) -> float:
return math.log2(self.d_a)
def sample_np(
self, rng: np.random.Generator, batch: int
) -> tuple[np.ndarray, np.ndarray]:
"""
Sample haars-random pure states on C^(d_A d_B), observable = entanglement entropy.
Parameters
----------
rng : np.random.Generator
Random number generator.
batch : int
Number of samples to generate.
Returns
-------
x : np.ndarray
Shape (batch, d_a * d_b), complex64.
y : np.ndarray
Shape (batch,), float32.
"""
g = rng.normal(size=(batch, self.d_a, self.d_b)) + 1j * rng.normal(
size=(batch, self.d_a, self.d_b)
)
g = (g / math.sqrt(2.0)).astype(np.complex64)
g /= np.sqrt(np.sum(np.abs(g) ** 2, axis=(1, 2), keepdims=True))
rho = g @ np.swapaxes(np.conj(g), 1, 2)
lam = np.clip(np.linalg.eigvalsh(rho).real, 1e-30, 1.0)
return g.reshape(batch, -1), entropy_bits_from_probs(lam, np).astype(np.float32)
def sample_jax(self, key: Any, batch: int) -> tuple[Any, Any]:
k1, k2 = random.split(key)
g = (
random.normal(k1, (batch, self.d_a, self.d_b), dtype=jnp.float32)
+ 1j * random.normal(k2, (batch, self.d_a, self.d_b), dtype=jnp.float32)
) / math.sqrt(2.0)
g = g / jnp.sqrt(jnp.sum(jnp.abs(g) ** 2, axis=(1, 2), keepdims=True))
rho = g @ jnp.swapaxes(jnp.conj(g), -1, -2)
lam = jnp.clip(jnp.linalg.eigvalsh(rho).real, 1e-30, 1.0)
return g.reshape(batch, -1), entropy_bits_from_probs(lam, jnp)
def metric_pairs(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
return fs_metric_np(x, y)
def theory(self, kappa: float) -> dict[str, float]:
d = self.d_a * self.d_b
beta = self.d_a / (math.log(2.0) * self.d_b)
alpha = (math.log2(self.d_a) / math.sqrt(HAYDEN_C * (d - 1.0))) * math.sqrt(
math.log(1.0 / kappa)
)
tail = sum(1.0 / k for k in range(self.d_b + 1, d + 1))
page = (tail - (self.d_a - 1.0) / (2.0 * self.d_b)) / math.log(2.0)
return {
"page_average_bits": page,
"hayden_mean_lower_bits": math.log2(self.d_a) - beta,
"hayden_cutoff_bits": math.log2(self.d_a) - (beta + alpha),
"hayden_one_sided_width_bits": beta + alpha,
"levy_scaling_width_bits": 2.0
* (math.log2(self.d_a) / math.sqrt(HAYDEN_C * (d - 1.0)))
* math.sqrt(math.log(2.0 / kappa)),
}
def tail_bound(self, deficits: np.ndarray) -> np.ndarray:
beta = self.d_a / (math.log(2.0) * self.d_b)
shifted = np.maximum(np.asarray(deficits, float) - beta, 0.0)
expo = (
-(self.d_a * self.d_b - 1.0)
* HAYDEN_C
* shifted**2
/ (math.log2(self.d_a) ** 2)
)
out = np.exp(expo)
out[deficits <= beta] = 1.0
return np.clip(out, 0.0, 1.0)
@dataclass
class MajoranaSymmetricSpace(MetricMeasureSpace):
"""Haar-random symmetric N-qubit states; stars are for visualization only."""
N: int
family: str = "majorana"
@property
def label(self) -> str:
return f"Sym^{self.N}(C^2) ≅ CP^{self.N}"
@property
def slug(self) -> str:
return f"majorana_{self.N}"
@property
def intrinsic_dim(self) -> int:
return self.N
@property
def state_dim(self) -> int:
return self.N + 1
@property
def observable_max(self) -> float:
return 1.0 # one-qubit entropy upper bound
def _rho1_np(self, c: np.ndarray) -> np.ndarray:
k = np.arange(self.N + 1, dtype=np.float32)
p = np.abs(c) ** 2
rho11 = (p * k).sum(axis=1) / self.N
coef = (
np.sqrt(
(np.arange(self.N, dtype=np.float32) + 1.0)
* (self.N - np.arange(self.N, dtype=np.float32))
)
/ self.N
)
off = (np.conj(c[:, :-1]) * c[:, 1:] * coef).sum(axis=1)
rho = np.zeros((len(c), 2, 2), dtype=np.complex64)
rho[:, 0, 0] = 1.0 - rho11
rho[:, 1, 1] = rho11
rho[:, 0, 1] = off
rho[:, 1, 0] = np.conj(off)
return rho
def _rho1_jax(self, c: Any) -> Any:
k = jnp.arange(self.N + 1, dtype=jnp.float32)
p = jnp.abs(c) ** 2
rho11 = jnp.sum(p * k, axis=1) / self.N
kk = jnp.arange(self.N, dtype=jnp.float32)
coef = jnp.sqrt((kk + 1.0) * (self.N - kk)) / self.N
off = jnp.sum(jnp.conj(c[:, :-1]) * c[:, 1:] * coef, axis=1)
rho = jnp.zeros((c.shape[0], 2, 2), dtype=jnp.complex64)
rho = rho.at[:, 0, 0].set(1.0 - rho11)
rho = rho.at[:, 1, 1].set(rho11)
rho = rho.at[:, 0, 1].set(off)
rho = rho.at[:, 1, 0].set(jnp.conj(off))
return rho
def sample_np(
self, rng: np.random.Generator, batch: int
) -> tuple[np.ndarray, np.ndarray]:
c = rng.normal(size=(batch, self.N + 1)) + 1j * rng.normal(
size=(batch, self.N + 1)
)
c = (c / math.sqrt(2.0)).astype(np.complex64)
c /= np.linalg.norm(c, axis=1, keepdims=True)
lam = np.clip(np.linalg.eigvalsh(self._rho1_np(c)).real, 1e-30, 1.0)
return c, entropy_bits_from_probs(lam, np).astype(np.float32)
def sample_jax(self, key: Any, batch: int) -> tuple[Any, Any]:
k1, k2 = random.split(key)
c = (
random.normal(k1, (batch, self.N + 1), dtype=jnp.float32)
+ 1j * random.normal(k2, (batch, self.N + 1), dtype=jnp.float32)
) / math.sqrt(2.0)
c = c / jnp.linalg.norm(c, axis=1, keepdims=True)
lam = jnp.clip(jnp.linalg.eigvalsh(self._rho1_jax(c)).real, 1e-30, 1.0)
return c, entropy_bits_from_probs(lam, jnp)
def metric_pairs(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
return fs_metric_np(x, y)
def majorana_stars(self, coeffs: np.ndarray) -> np.ndarray:
"""Map one symmetric state to its Majorana stars on S^2."""
a = np.array(
[
((-1) ** k) * math.sqrt(math.comb(self.N, k)) * coeffs[k]
for k in range(self.N + 1)
],
np.complex128,
)
poly = np.trim_zeros(a[::-1], trim="f")
roots = np.roots(poly) if len(poly) > 1 else np.empty(0, dtype=np.complex128)
r2 = np.abs(roots) ** 2
pts = np.c_[
2 * roots.real / (1 + r2), 2 * roots.imag / (1 + r2), (r2 - 1) / (1 + r2)
]
missing = self.N - len(pts)
if missing > 0:
pts = np.vstack([pts, np.tile(np.array([[0.0, 0.0, 1.0]]), (missing, 1))])
return pts.astype(np.float32)

96
codes/reference/plot_entropy_and_alpha.py
View File

@@ -1,48 +1,48 @@
"""
plot the probability of the entropy of the reduced density matrix of the pure state being greater than log2(d_A) - alpha - beta
for different alpha values
IGNORE THE CONSTANT C
NOTE there is bug in the program, You should fix it if you want to use the visualization, it relates to the alpha range and you should not plot the prob of 0
"""
import numpy as np
import matplotlib.pyplot as plt
from quantum_states import sample_and_calculate
from tqdm import tqdm
# Set dimensions
db = 16
da_values = [8, 16, 32]
alpha_range = np.linspace(0, 2, 100) # Range of alpha values to plot
n_samples = 100000
plt.figure(figsize=(10, 6))
for da in tqdm(da_values, desc="Processing d_A values"):
# Calculate beta according to the formula
beta = da / (np.log(2) * db)
# Calculate probability for each alpha
predicted_probabilities = []
actual_probabilities = []
for alpha in tqdm(alpha_range, desc=f"Calculating probabilities for d_A={da}", leave=False):
# Calculate probability according to the formula
# Ignoring constant C as requested
prob = np.exp(-(da * db - 1) * alpha**2 / (np.log2(da))**2)
predicted_probabilities.append(prob)
# Calculate actual probability
entropies = sample_and_calculate(da, db, n_samples=n_samples)
actual_probabilities.append(np.sum(entropies > np.log2(da) - alpha - beta) / n_samples)
# plt.plot(alpha_range, predicted_probabilities, label=f'$d_A={da}$', linestyle='--')
plt.plot(alpha_range, actual_probabilities, label=f'$d_A={da}$', linestyle='-')
plt.xlabel(r'$\alpha$')
plt.ylabel('Probability')
plt.title(r'$\operatorname{Pr}[H(\psi_A) <\log_2(d_A)-\alpha-\beta]$ vs $\alpha$ for different $d_A$')
plt.legend()
plt.grid(True)
plt.yscale('log') # Use log scale for better visualization
plt.show()
"""
plot the probability of the entropy of the reduced density matrix of the pure state being greater than log2(d_A) - alpha - beta
for different alpha values
IGNORE THE CONSTANT C
NOTE there is bug in the program, You should fix it if you want to use the visualization, it relates to the alpha range and you should not plot the prob of 0
"""
import numpy as np
import matplotlib.pyplot as plt
from quantum_states import sample_and_calculate
from tqdm import tqdm
# Set dimensions
db = 16
da_values = [8, 16, 32]
alpha_range = np.linspace(0, 2, 100) # Range of alpha values to plot
n_samples = 100000
plt.figure(figsize=(10, 6))
for da in tqdm(da_values, desc="Processing d_A values"):
# Calculate beta according to the formula
beta = da / (np.log(2) * db)
# Calculate probability for each alpha
predicted_probabilities = []
actual_probabilities = []
for alpha in tqdm(alpha_range, desc=f"Calculating probabilities for d_A={da}", leave=False):
# Calculate probability according to the formula
# Ignoring constant C as requested
prob = np.exp(-(da * db - 1) * alpha**2 / (np.log2(da))**2)
predicted_probabilities.append(prob)
# Calculate actual probability
entropies = sample_and_calculate(da, db, n_samples=n_samples)
actual_probabilities.append(np.sum(entropies > np.log2(da) - alpha - beta) / n_samples)
# plt.plot(alpha_range, predicted_probabilities, label=f'$d_A={da}$', linestyle='--')
plt.plot(alpha_range, actual_probabilities, label=f'$d_A={da}$', linestyle='-')
plt.xlabel(r'$\alpha$')
plt.ylabel('Probability')
plt.title(r'$\operatorname{Pr}[H(\psi_A) <\log_2(d_A)-\alpha-\beta]$ vs $\alpha$ for different $d_A$')
plt.legend()
plt.grid(True)
plt.yscale('log') # Use log scale for better visualization
plt.show()

104
codes/reference/plot_entropy_and_da.py
View File

@@ -1,52 +1,52 @@
"""
plot the probability of the entropy of the reduced density matrix of the pure state being greater than log2(d_A) - alpha - beta
for different d_A values, with fixed alpha and d_B Note, d_B>d_A
"""
import numpy as np
import matplotlib.pyplot as plt
from quantum_states import sample_and_calculate
from tqdm import tqdm
# Set dimensions
db = 32
alpha = 0
da_range = np.arange(2, 10, 1) # Range of d_A values to plot
n_samples = 1000000
plt.figure(figsize=(10, 6))
predicted_probabilities = []
actual_probabilities = []
for da in tqdm(da_range, desc="Processing d_A values"):
# Calculate beta according to the formula
beta = da / (np.log(2) * db)
# Calculate probability according to the formula
# Ignoring constant C as requested
prob = np.exp(-((da * db - 1) * alpha**2 / (np.log2(da)**2)))
predicted_probabilities.append(prob)
# Calculate actual probability
entropies = sample_and_calculate(da, db, n_samples=n_samples)
count = np.sum(entropies < np.log2(da) - alpha - beta)
# early stop if count is 0
if count != 0:
actual_probabilities.append(count / n_samples)
else:
actual_probabilities.extend([np.nan] * (len(da_range) - len(actual_probabilities)))
break
# debug
print(f'da={da}, theoretical_prob={prob}, threshold={np.log2(da) - alpha - beta}, actual_prob={actual_probabilities[-1]}, entropy_heads={entropies[:10]}')
# plt.plot(da_range, predicted_probabilities, label=f'$d_A={da}$', linestyle='--')
plt.plot(da_range, actual_probabilities, label=f'$d_A={da}$', linestyle='-')
plt.xlabel(r'$d_A$')
plt.ylabel('Probability')
plt.title(r'$\operatorname{Pr}[H(\psi_A) < \log_2(d_A)-\alpha-\beta]$ vs $d_A$ for fixed $\alpha=$'+str(alpha)+r' and $d_B=$' +str(db)+ r' with $n=$' +str(n_samples))
# plt.legend()
plt.grid(True)
plt.yscale('log') # Use log scale for better visualization
plt.show()
"""
plot the probability of the entropy of the reduced density matrix of the pure state being greater than log2(d_A) - alpha - beta
for different d_A values, with fixed alpha and d_B Note, d_B>d_A
"""
import numpy as np
import matplotlib.pyplot as plt
from quantum_states import sample_and_calculate
from tqdm import tqdm
# Set dimensions
db = 32
alpha = 0
da_range = np.arange(2, 10, 1) # Range of d_A values to plot
n_samples = 1000000
plt.figure(figsize=(10, 6))
predicted_probabilities = []
actual_probabilities = []
for da in tqdm(da_range, desc="Processing d_A values"):
# Calculate beta according to the formula
beta = da / (np.log(2) * db)
# Calculate probability according to the formula
# Ignoring constant C as requested
prob = np.exp(-((da * db - 1) * alpha**2 / (np.log2(da)**2)))
predicted_probabilities.append(prob)
# Calculate actual probability
entropies = sample_and_calculate(da, db, n_samples=n_samples)
count = np.sum(entropies < np.log2(da) - alpha - beta)
# early stop if count is 0
if count != 0:
actual_probabilities.append(count / n_samples)
else:
actual_probabilities.extend([np.nan] * (len(da_range) - len(actual_probabilities)))
break
# debug
print(f'da={da}, theoretical_prob={prob}, threshold={np.log2(da) - alpha - beta}, actual_prob={actual_probabilities[-1]}, entropy_heads={entropies[:10]}')
# plt.plot(da_range, predicted_probabilities, label=f'$d_A={da}$', linestyle='--')
plt.plot(da_range, actual_probabilities, label=f'$d_A={da}$', linestyle='-')
plt.xlabel(r'$d_A$')
plt.ylabel('Probability')
plt.title(r'$\operatorname{Pr}[H(\psi_A) < \log_2(d_A)-\alpha-\beta]$ vs $d_A$ for fixed $\alpha=$'+str(alpha)+r' and $d_B=$' +str(db)+ r' with $n=$' +str(n_samples))
# plt.legend()
plt.grid(True)
plt.yscale('log') # Use log scale for better visualization
plt.show()

110
codes/reference/plot_entropy_and_deviate.py
View File

@@ -1,55 +1,55 @@
import numpy as np
import matplotlib.pyplot as plt
from quantum_states import sample_and_calculate
from tqdm import tqdm
# Set dimensions, keep db\geq da\geq 3
db = 64
da_values = [4, 8, 16, 32]
da_colors = ['b', 'g', 'r', 'c']
n_samples = 100000
plt.figure(figsize=(10, 6))
# Define range of deviations to test (in bits)
deviations = np.linspace(0, 1, 50) # Test deviations from 0 to 1 bits
for i, da in enumerate(tqdm(da_values, desc="Processing d_A values")):
# Calculate maximal entropy
max_entropy = np.log2(min(da, db))
# Sample random states and calculate their entropies
entropies = sample_and_calculate(da, db, n_samples=n_samples)
# Calculate probabilities for each deviation
probabilities = []
theoretical_probs = []
for dev in deviations:
# Count states that deviate by more than dev bits from max entropy
count = np.sum(max_entropy - entropies > dev)
# Omit the case where count is 0
if count != 0:
prob = count / len(entropies)
probabilities.append(prob)
else:
probabilities.append(np.nan)
# Calculate theoretical probability using concentration inequality
# note max_entropy - dev = max_entropy - beta - alpha, so alpha = dev - beta
beta = da / (np.log(2)*db)
alpha = dev - beta
theoretical_prob = np.exp(-(da * db - 1) * alpha**2 / (np.log2(da))**2)
# # debug
# print(f"dev: {dev}, beta: {beta}, alpha: {alpha}, theoretical_prob: {theoretical_prob}")
theoretical_probs.append(theoretical_prob)
plt.plot(deviations, probabilities, '-', label=f'$d_A={da}$ (simulated)', color=da_colors[i])
plt.plot(deviations, theoretical_probs, '--', label=f'$d_A={da}$ (theoretical)', color=da_colors[i])
plt.xlabel('Deviation from maximal entropy (bits)')
plt.ylabel('Probability')
plt.title(f'Probability of deviation from maximal entropy simulation with sample size {n_samples} for $d_B={db}$ ignoring the constant $C$')
plt.legend()
plt.grid(True)
plt.yscale('log') # Use log scale for better visualization
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from quantum_states import sample_and_calculate
from tqdm import tqdm
# Set dimensions, keep db\geq da\geq 3
db = 64
da_values = [4, 8, 16, 32]
da_colors = ['b', 'g', 'r', 'c']
n_samples = 100000
plt.figure(figsize=(10, 6))
# Define range of deviations to test (in bits)
deviations = np.linspace(0, 1, 50) # Test deviations from 0 to 1 bits
for i, da in enumerate(tqdm(da_values, desc="Processing d_A values")):
# Calculate maximal entropy
max_entropy = np.log2(min(da, db))
# Sample random states and calculate their entropies
entropies = sample_and_calculate(da, db, n_samples=n_samples)
# Calculate probabilities for each deviation
probabilities = []
theoretical_probs = []
for dev in deviations:
# Count states that deviate by more than dev bits from max entropy
count = np.sum(max_entropy - entropies > dev)
# Omit the case where count is 0
if count != 0:
prob = count / len(entropies)
probabilities.append(prob)
else:
probabilities.append(np.nan)
# Calculate theoretical probability using concentration inequality
# note max_entropy - dev = max_entropy - beta - alpha, so alpha = dev - beta
beta = da / (np.log(2)*db)
alpha = dev - beta
theoretical_prob = np.exp(-(da * db - 1) * alpha**2 / (np.log2(da))**2)
# # debug
# print(f"dev: {dev}, beta: {beta}, alpha: {alpha}, theoretical_prob: {theoretical_prob}")
theoretical_probs.append(theoretical_prob)
plt.plot(deviations, probabilities, '-', label=f'$d_A={da}$ (simulated)', color=da_colors[i])
plt.plot(deviations, theoretical_probs, '--', label=f'$d_A={da}$ (theoretical)', color=da_colors[i])
plt.xlabel('Deviation from maximal entropy (bits)')
plt.ylabel('Probability')
plt.title(f'Probability of deviation from maximal entropy simulation with sample size {n_samples} for $d_B={db}$ ignoring the constant $C$')
plt.legend()
plt.grid(True)
plt.yscale('log') # Use log scale for better visualization
plt.show()

66
codes/reference/plot_entropy_and_dim.py
View File

@@ -1,33 +1,33 @@
import numpy as np
import matplotlib.pyplot as plt
from quantum_states import sample_and_calculate
from tqdm import tqdm
# Define range of dimensions to test
fixed_dim = 64
dimensions = np.arange(2, 64, 2) # Test dimensions from 2 to 50 in steps of 2
expected_entropies = []
theoretical_entropies = []
predicted_entropies = []
# Calculate entropies for each dimension
for dim in tqdm(dimensions, desc="Calculating entropies"):
# For each dimension, we'll keep one subsystem fixed at dim=2
# and vary the other dimension
entropies = sample_and_calculate(dim, fixed_dim, n_samples=1000)
expected_entropies.append(np.mean(entropies))
theoretical_entropies.append(np.log2(min(dim, fixed_dim)))
beta = min(dim, fixed_dim)/(2*np.log(2)*max(dim, fixed_dim))
predicted_entropies.append(np.log2(min(dim, fixed_dim)) - beta)
# Create the plot
plt.figure(figsize=(10, 6))
plt.plot(dimensions, expected_entropies, 'b-', label='Expected Entropy')
plt.plot(dimensions, theoretical_entropies, 'r--', label='Theoretical Entropy')
plt.plot(dimensions, predicted_entropies, 'g--', label='Predicted Entropy')
plt.xlabel('Dimension of Subsystem B')
plt.ylabel('von Neumann Entropy (bits)')
plt.title(f'von Neumann Entropy vs. System Dimension, with Dimension of Subsystem A = {fixed_dim}')
plt.legend()
plt.grid(True)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from quantum_states import sample_and_calculate
from tqdm import tqdm
# Define range of dimensions to test
fixed_dim = 64
dimensions = np.arange(2, 64, 2) # Test dimensions from 2 to 50 in steps of 2
expected_entropies = []
theoretical_entropies = []
predicted_entropies = []
# Calculate entropies for each dimension
for dim in tqdm(dimensions, desc="Calculating entropies"):
# For each dimension, we'll keep one subsystem fixed at dim=2
# and vary the other dimension
entropies = sample_and_calculate(dim, fixed_dim, n_samples=1000)
expected_entropies.append(np.mean(entropies))
theoretical_entropies.append(np.log2(min(dim, fixed_dim)))
beta = min(dim, fixed_dim)/(2*np.log(2)*max(dim, fixed_dim))
predicted_entropies.append(np.log2(min(dim, fixed_dim)) - beta)
# Create the plot
plt.figure(figsize=(10, 6))
plt.plot(dimensions, expected_entropies, 'b-', label='Expected Entropy')
plt.plot(dimensions, theoretical_entropies, 'r--', label='Theoretical Entropy')
plt.plot(dimensions, predicted_entropies, 'g--', label='Predicted Entropy')
plt.xlabel('Dimension of Subsystem B')
plt.ylabel('von Neumann Entropy (bits)')
plt.title(f'von Neumann Entropy vs. System Dimension, with Dimension of Subsystem A = {fixed_dim}')
plt.legend()
plt.grid(True)
plt.show()

100
codes/reference/plot_entropy_and_dim_3d.py
View File

@@ -1,51 +1,51 @@
import numpy as np
import matplotlib.pyplot as plt
from quantum_states import sample_and_calculate
from tqdm import tqdm
from mpl_toolkits.mplot3d import Axes3D
# Define range of dimensions to test
dimensionsA = np.arange(2, 64, 2) # Test dimensions from 2 to 50 in steps of 2
dimensionsB = np.arange(2, 64, 2) # Test dimensions from 2 to 50 in steps of 2
# Create meshgrid for 3D plot
X, Y = np.meshgrid(dimensionsA, dimensionsB)
Z = np.zeros_like(X, dtype=float)
# Calculate entropies for each dimension combination
total_iterations = len(dimensionsA) * len(dimensionsB)
pbar = tqdm(total=total_iterations, desc="Calculating entropies")
for i, dim_a in enumerate(dimensionsA):
for j, dim_b in enumerate(dimensionsB):
entropies = sample_and_calculate(dim_a, dim_b, n_samples=100)
Z[j,i] = np.mean(entropies)
pbar.update(1)
pbar.close()
# Create the 3D plot
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
# Plot the surface
surf = ax.plot_surface(X, Y, Z, cmap='viridis')
# Add labels and title with larger font sizes
ax.set_xlabel('Dimension of Subsystem A', fontsize=12, labelpad=10)
ax.set_ylabel('Dimension of Subsystem B', fontsize=12, labelpad=10)
ax.set_zlabel('von Neumann Entropy (bits)', fontsize=12, labelpad=10)
ax.set_title('von Neumann Entropy vs. System Dimensions', fontsize=14, pad=20)
# Add colorbar
cbar = fig.colorbar(surf, ax=ax, label='Entropy')
cbar.ax.set_ylabel('Entropy', fontsize=12)
# Add tick labels with larger font size
ax.tick_params(axis='x', labelsize=10)
ax.tick_params(axis='y', labelsize=10)
ax.tick_params(axis='z', labelsize=10)
# Rotate the plot for better visibility
ax.view_init(elev=30, azim=45)
import numpy as np
import matplotlib.pyplot as plt
from quantum_states import sample_and_calculate
from tqdm import tqdm
from mpl_toolkits.mplot3d import Axes3D
# Define range of dimensions to test
dimensionsA = np.arange(2, 64, 2) # Test dimensions from 2 to 50 in steps of 2
dimensionsB = np.arange(2, 64, 2) # Test dimensions from 2 to 50 in steps of 2
# Create meshgrid for 3D plot
X, Y = np.meshgrid(dimensionsA, dimensionsB)
Z = np.zeros_like(X, dtype=float)
# Calculate entropies for each dimension combination
total_iterations = len(dimensionsA) * len(dimensionsB)
pbar = tqdm(total=total_iterations, desc="Calculating entropies")
for i, dim_a in enumerate(dimensionsA):
for j, dim_b in enumerate(dimensionsB):
entropies = sample_and_calculate(dim_a, dim_b, n_samples=100)
Z[j,i] = np.mean(entropies)
pbar.update(1)
pbar.close()
# Create the 3D plot
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
# Plot the surface
surf = ax.plot_surface(X, Y, Z, cmap='viridis')
# Add labels and title with larger font sizes
ax.set_xlabel('Dimension of Subsystem A', fontsize=12, labelpad=10)
ax.set_ylabel('Dimension of Subsystem B', fontsize=12, labelpad=10)
ax.set_zlabel('von Neumann Entropy (bits)', fontsize=12, labelpad=10)
ax.set_title('von Neumann Entropy vs. System Dimensions', fontsize=14, pad=20)
# Add colorbar
cbar = fig.colorbar(surf, ax=ax, label='Entropy')
cbar.ax.set_ylabel('Entropy', fontsize=12)
# Add tick labels with larger font size
ax.tick_params(axis='x', labelsize=10)
ax.tick_params(axis='y', labelsize=10)
ax.tick_params(axis='z', labelsize=10)
# Rotate the plot for better visibility
ax.view_init(elev=30, azim=45)
plt.show()

192
codes/reference/quantum_states.py
View File

@@ -1,96 +1,96 @@
import numpy as np
from scipy.linalg import sqrtm
from scipy.stats import unitary_group
from tqdm import tqdm
def random_pure_state(dim_a, dim_b):
"""
Generate a random pure state for a bipartite system.
The random pure state is uniformly distributed by the Haar (Fubini-Study) measure on the unit sphere $S^{dim_a * dim_b - 1}$. (Invariant under the unitary group $U(dim_a) \times U(dim_b)$)
Args:
dim_a (int): Dimension of subsystem A
dim_b (int): Dimension of subsystem B
Returns:
numpy.ndarray: Random pure state vector of shape (dim_a * dim_b,)
"""
# Total dimension of the composite system
dim_total = dim_a * dim_b
# Generate non-zero random complex vector
while True:
state = np.random.normal(size=(dim_total,)) + 1j * np.random.normal(size=(dim_total,))
if np.linalg.norm(state) > 0:
break
# Normalize the state
state = state / np.linalg.norm(state)
return state
def von_neumann_entropy_bipartite_pure_state(state, dim_a, dim_b):
"""
Calculate the von Neumann entropy of the reduced density matrix.
Args:
state (numpy.ndarray): Pure state vector
dim_a (int): Dimension of subsystem A
dim_b (int): Dimension of subsystem B
Returns:
float: Von Neumann entropy
"""
# Reshape state vector to matrix form
state_matrix = state.reshape(dim_a, dim_b)
# Calculate reduced density matrix of subsystem A
rho_a = np.dot(state_matrix, state_matrix.conj().T)
# Calculate eigenvalues
eigenvals = np.linalg.eigvalsh(rho_a)
# Remove very small eigenvalues (numerical errors)
eigenvals = eigenvals[eigenvals > 1e-15]
# Calculate von Neumann entropy
entropy = -np.sum(eigenvals * np.log2(eigenvals))
return np.real(entropy)
def sample_and_calculate(dim_a, dim_b, n_samples=1000):
"""
Sample random pure states (generate random co) and calculate their von Neumann entropy.
Args:
dim_a (int): Dimension of subsystem A
dim_b (int): Dimension of subsystem B
n_samples (int): Number of samples to generate
Returns:
numpy.ndarray: Array of entropy values
"""
entropies = np.zeros(n_samples)
for i in tqdm(range(n_samples), desc=f"Sampling states (d_A={dim_a}, d_B={dim_b})", leave=False):
state = random_pure_state(dim_a, dim_b)
entropies[i] = von_neumann_entropy_bipartite_pure_state(state, dim_a, dim_b)
return entropies
# Example usage:
if __name__ == "__main__":
# Example: 2-qubit system
dim_a, dim_b = 50,100
# Generate single random state and calculate entropy
state = random_pure_state(dim_a, dim_b)
entropy = von_neumann_entropy_bipartite_pure_state(state, dim_a, dim_b)
print(f"Single state entropy: {entropy}")
# Sample multiple states
entropies = sample_and_calculate(dim_a, dim_b, n_samples=1000)
print(f"Expected entropy: {np.mean(entropies)}")
print(f"Theoretical entropy: {np.log2(max(dim_a, dim_b))}")
print(f"Standard deviation: {np.std(entropies)}")
import numpy as np
from scipy.linalg import sqrtm
from scipy.stats import unitary_group
from tqdm import tqdm
def random_pure_state(dim_a, dim_b):
"""
Generate a random pure state for a bipartite system.
The random pure state is uniformly distributed by the Haar (Fubini-Study) measure on the unit sphere $S^{dim_a * dim_b - 1}$. (Invariant under the unitary group $U(dim_a) \times U(dim_b)$)
Args:
dim_a (int): Dimension of subsystem A
dim_b (int): Dimension of subsystem B
Returns:
numpy.ndarray: Random pure state vector of shape (dim_a * dim_b,)
"""
# Total dimension of the composite system
dim_total = dim_a * dim_b
# Generate non-zero random complex vector
while True:
state = np.random.normal(size=(dim_total,)) + 1j * np.random.normal(size=(dim_total,))
if np.linalg.norm(state) > 0:
break
# Normalize the state
state = state / np.linalg.norm(state)
return state
def von_neumann_entropy_bipartite_pure_state(state, dim_a, dim_b):
"""
Calculate the von Neumann entropy of the reduced density matrix.
Args:
state (numpy.ndarray): Pure state vector
dim_a (int): Dimension of subsystem A
dim_b (int): Dimension of subsystem B
Returns:
float: Von Neumann entropy
"""
# Reshape state vector to matrix form
state_matrix = state.reshape(dim_a, dim_b)
# Calculate reduced density matrix of subsystem A
rho_a = np.dot(state_matrix, state_matrix.conj().T)
# Calculate eigenvalues
eigenvals = np.linalg.eigvalsh(rho_a)
# Remove very small eigenvalues (numerical errors)
eigenvals = eigenvals[eigenvals > 1e-15]
# Calculate von Neumann entropy
entropy = -np.sum(eigenvals * np.log2(eigenvals))
return np.real(entropy)
def sample_and_calculate(dim_a, dim_b, n_samples=1000):
"""
Sample random pure states (generate random co) and calculate their von Neumann entropy.
Args:
dim_a (int): Dimension of subsystem A
dim_b (int): Dimension of subsystem B
n_samples (int): Number of samples to generate
Returns:
numpy.ndarray: Array of entropy values
"""
entropies = np.zeros(n_samples)
for i in tqdm(range(n_samples), desc=f"Sampling states (d_A={dim_a}, d_B={dim_b})", leave=False):
state = random_pure_state(dim_a, dim_b)
entropies[i] = von_neumann_entropy_bipartite_pure_state(state, dim_a, dim_b)
return entropies
# Example usage:
if __name__ == "__main__":
# Example: 2-qubit system
dim_a, dim_b = 50,100
# Generate single random state and calculate entropy
state = random_pure_state(dim_a, dim_b)
entropy = von_neumann_entropy_bipartite_pure_state(state, dim_a, dim_b)
print(f"Single state entropy: {entropy}")
# Sample multiple states
entropies = sample_and_calculate(dim_a, dim_b, n_samples=1000)
print(f"Expected entropy: {np.mean(entropies)}")
print(f"Theoretical entropy: {np.log2(max(dim_a, dim_b))}")
print(f"Standard deviation: {np.std(entropies)}")

62
codes/reference/test.py
View File

@@ -1,32 +1,32 @@
# unit test for the functions in quantum_states.py
import unittest
import numpy as np
from quantum_states import random_pure_state, von_neumann_entropy_bipartite_pure_state
class LearningCase(unittest.TestCase):
def test_random_pure_state_shape_and_norm(self):
dim_a = 2
dim_b = 2
state = random_pure_state(dim_a, dim_b)
self.assertEqual(state.shape, (dim_a * dim_b,))
self.assertAlmostEqual(np.linalg.norm(state), 1)
def test_partial_trace_entropy(self):
dim_a = 2
dim_b = 2
state = random_pure_state(dim_a, dim_b)
self.assertAlmostEqual(von_neumann_entropy_bipartite_pure_state(state, dim_a, dim_b), von_neumann_entropy_bipartite_pure_state(state, dim_b, dim_a))
def test_sample_uniformly(self):
# calculate the distribution of the random pure state
dim_a = 2
dim_b = 2
state = random_pure_state(dim_a, dim_b)
def main():
unittest.main()
if __name__ == "__main__":
# unit test for the functions in quantum_states.py
import unittest
import numpy as np
from quantum_states import random_pure_state, von_neumann_entropy_bipartite_pure_state
class LearningCase(unittest.TestCase):
def test_random_pure_state_shape_and_norm(self):
dim_a = 2
dim_b = 2
state = random_pure_state(dim_a, dim_b)
self.assertEqual(state.shape, (dim_a * dim_b,))
self.assertAlmostEqual(np.linalg.norm(state), 1)
def test_partial_trace_entropy(self):
dim_a = 2
dim_b = 2
state = random_pure_state(dim_a, dim_b)
self.assertAlmostEqual(von_neumann_entropy_bipartite_pure_state(state, dim_a, dim_b), von_neumann_entropy_bipartite_pure_state(state, dim_b, dim_a))
def test_sample_uniformly(self):
# calculate the distribution of the random pure state
dim_a = 2
dim_b = 2
state = random_pure_state(dim_a, dim_b)
def main():
unittest.main()
if __name__ == "__main__":
main()

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% chapters/chap2.tex
\documentclass[../main.tex]{subfiles}
\ifSubfilesClassLoaded{
\addbibresource{../main.bib}
}
\begin{document}
\chapter{Levy's family and observable diameters}
In this section, we will explore how the results from Hayden's concentration of measure theorem can be understood in terms of observable diameters from Gromov's perspective and what properties it reveals for entropy functions.
We will try to use the results from previous sections to estimate the observable diameter for complex projective spaces.
\section{Observable diameters}
Recall from previous sections, an arbitrary 1-Lipschitz function $f:S^n\to \mathbb{R}$ concentrates near a single value $a_0\in \mathbb{R}$ as strongly as the distance function does.
\begin{defn}
\label{defn:mm-space}
Let $X$ be a topological space with the following:
\begin{enumerate}
\item $X$ is a complete (every Cauchy sequence converges)
\item $X$ is a metric space with metric $d_X$
\item $X$ has a Borel probability measure $\mu_X$
\end{enumerate}
Then $(X,d_X,\mu_X)$ is a \textbf{metric measure space}.
\end{defn}
\begin{defn}
\label{defn:diameter}
Let $(X,d_X)$ be a metric space. The \textbf{diameter} of a set $A\subset X$ is defined as
$$
\diam(A)=\sup_{x,y\in A}d_X(x,y).
$$
\end{defn}
\begin{defn}
\label{defn:partial-diameter}
Let $(X,d_X,\mu_X)$ be a metric measure space, For any real number $\alpha\leq 1$, the \textbf{partial diameter} of $X$ is defined as
$$
\diam(A;\alpha)=\inf_{A\subseteq X|\mu_X(A)\geq \alpha}\diam(A).
$$
\end{defn}
This definition generalize the relation between the measure and metric in the metric-measure space. Intuitively, the space with smaller partial diameter can take more volume with the same diameter constrains.
However, in higher dimensions, the volume may tend to concentrates more around a small neighborhood of the set, as we see in previous chapters with high dimensional sphere as example. We can safely cut $\kappa>0$ volume to significantly reduce the diameter, this yields better measure for concentration for shapes in spaces with high dimension.
\begin{defn}
\label{defn:observable-diameter}
Let $X$ be a metric-measure space, $Y$ be a metric space, and $f:X\to Y$ be a 1-Lipschitz function. Then $f_*\mu_X=\mu_Y$ is a push forward measure on $Y$.
For any real number $\kappa>0$, the \textbf{$\kappa$-observable diameter with screen $Y$} is defined as
$$
\obdiam_Y(X;\kappa)=\sup\{\diam(f_*\mu_X;1-\kappa)\}
$$
And the \textbf{obbservable diameter with screen $Y$} is defined as
$$
\obdiam_Y(X)=\inf_{\kappa>0}\max\{\obdiam_Y(X;\kappa)\}
$$
If $Y=\R$, we call it the \textbf{observable diameter}.
\end{defn}
If we collapse it naively via
$$
\inf_{\kappa>0}\obdiam_Y(X;\kappa),
$$
we typically get something degenerate: as $\kappa\to 1$, the condition ``mass $\ge 1-\kappa$'' becomes almost empty space, so $\diam(\nu;1-\kappa)$ can be forced to be $0$ (take a tiny set of positive mass), and hence the infimum tends to $0$ for essentially any non-atomic space.
This is why one either:
\begin{enumerate}
\item keeps $\obdiam_Y(X;\kappa)$ as a \emph{function of $\kappa$} (picking $\kappa$ to be small but not $0$), or
\item if one insists on a single number, balances ``spread'' against ``exceptional mass'' by defining $\obdiam_Y(X)=\inf_{\kappa>0}\max\{\obdiam_Y(X;\kappa),\kappa\}$ as above.
\end{enumerate}
The point of the $\max\{\cdot,\kappa\}$ is that it prevents cheating by taking $\kappa$ close to $1$: if $\kappa$ is large then the maximum is large regardless of how small $\obdiam_Y(X;\kappa)$ is, so the infimum is forced to occur where the exceptional mass and the observable spread are small.
Few additional proposition in \cite{shioya2014metricmeasuregeometry} will help us to estimate the observable diameter for complex projective spaces.
\begin{prop}
\label{prop:observable-diameter-domination}
Let $X$ and $Y$ be two metric-measure spaces and $\kappa>0$, and let $f:Y\to X$ be a 1-Lipschitz function ($Y$ dominates $X$, denoted as $X\prec Y$) then:
\begin{enumerate}
\item
$
\diam(X,1-\kappa)\leq \diam(Y,1-\kappa)
$
\item $\obdiam(X;-\kappa)\leq \diam(X;1-\kappa)$, and $\obdiam(X)$ is finite.
\item
$
\obdiam(X;-\kappa)\leq \obdiam(Y;-\kappa)
$
\end{enumerate}
\end{prop}
\begin{proof}
Since $f$ is 1-Lipschitz, we have $f_*\mu Y=\mu_X$. Let $A$ be any Borel set of $Y$ with $\mu_Y(A)\geq 1-\kappa$ and $\overline{f(A)}$ be the closure of $f(A)$ in $X$. We have $\mu_X(\overline{f(A)})=\mu_Y(f^{-1}(\overline{f(A)}))\geq \mu_Y(A)\geq 1-\kappa$ and by the 1-lipschitz property, $\diam(\overline{f(A)})\leq \diam(A)$, so $\diam(X;1-\kappa)\leq \diam(A)\leq \diam(Y;1-\kappa)$.
Let $g:X\to \R$ be any 1-lipschitz function, since $(\R,|\cdot|,g_*\mu_X)$ is dominated by $X$, $\diam(\R;1-\kappa)\leq \diam(X;1-\kappa)$. Therefore, $\obdiam(X;-\kappa)\leq \diam(X;1-\kappa)$.
and
$$
\diam(g_*\mu_X;1-\kappa)\leq \diam((f\circ g)_*\mu_Y;1-\kappa)\leq \obdiam(Y;1-\kappa)
$$
\end{proof}
\begin{prop}
\label{prop:observable-diameter-scale}
Let $X$ be an metric-measure space. Then for any real number $t>0$, we have
$$
\obdiam(tX;-\kappa)=t\obdiam(X;-\kappa)
$$
Where $tX=(X,tdX,\mu X)$.
\end{prop}
\begin{proof}
$$
\begin{aligned}
\obdiam(tX;-\kappa)&=\sup\{\diam(f_*\mu_X;1-\kappa)|f:tX\to \R \text{ is 1-Lipschitz}\}\\
&=\sup\{\diam(f_*\mu_X;1-\kappa)|t^{-1}f:X\to \R \text{ is 1-Lipschitz}\}\\
&=\sup\{\diam((tg)_*\mu_X;1-\kappa)|g:X\to \R \text{ is 1-Lipschitz}\}\\
&=t\sup\{\diam(g_*\mu_X;1-\kappa)|g:X\to \R \text{ is 1-Lipschitz}\}\\
&=t\obdiam(X;-\kappa)
\end{aligned}
$$
\end{proof}
\subsection{Observable diameter for class of spheres}
In this section, we will try to use the results from previous sections to estimate the observable diameter for class of spheres.
\begin{theorem}
\label{thm:observable-diameter-sphere}
For any real number $\kappa$ with $0<\kappa<1$, we have
$$
\obdiam(S^n(1);-\kappa)=O(\sqrt{n})
$$
\end{theorem}
\begin{proof}
First, recall that by maxwell boltzmann distribution, we have that for any $n>0$, let $I(r)$ denote the measure of standard gaussian measure on the interval $[0,r]$. Then we have
$$
\begin{aligned}
\lim_{n\to \infty} \obdiam(S^n(\sqrt{n});-\kappa)&=\lim_{n\to \infty} \sup\{\diam((\pi_{n,k})_*\sigma^n;1-\kappa)|\pi_{n,k} \text{ is 1-Lipschitz}\}\\
&=\lim_{n\to \infty} \sup\{\diam(\gamma^1;1-\kappa)|\gamma^1 \text{ is the standard gaussian measure}\}\\
&=\diam(\gamma^1;1-\kappa)\\
&=2I^{-1}(\frac{1-\kappa}{2})\text { cutting the extremum for normal distribution}\\
\end{aligned}
$$
By proposition \ref{prop:observable-diameter-scale}, we have
$$
\obdiam(S^n(\sqrt{n});-\kappa)=\sqrt{n}\obdiam(S^n(1);-\kappa)
$$
So $\obdiam(S^n(1);-\kappa)=\sqrt{n}(2I^{-1}(\frac{1-\kappa}{2}))=O(\sqrt{n})$.
\end{proof}
From the previous discussion, we see that the only remaining for finding observable diameter of $\C P^n$ is to find the lipchitz function that is isometric with consistent push-forward measure.
To find such metric, we need some additional results from previous sections.
A natural measure for $\C P^n$ is the normalized volume measure on $\C P^n$ induced from the Fubini-Study metric. \cite{lee_introduction_2018} Example 2.30
\begin{defn}
\label{defn:fubini-study-metric}
Let $n$ be a positive integer, and consider the complex projective space $\C P^n$ defined as the quotient space of $\C^{n+1}$ by the equivalence relation $z\sim z'$ if there exists $\lambda \in \C$ such that $z=\lambda z'$. The map $\pi:\C^{n+1}\setminus\{0\}\to \C P^n$ sending each point in $\C^{n+1}\setminus\{0\}$ to its span is surjective smooth submersion.
Identifying $\C^{n+1}$ with $\R^{2n+2}$ with its Euclidean metric, we can view the unit sphere $S^{2n+1}$ with its roud metric $\mathring{g}$ as an embedded Riemannian submanifold of $\C^{n+1}\setminus\{0\}$. Let $p:S^{2n+1}\to \C P^n$ denote the restriction of the map $\pi$. Then $p$ is smooth, and its is surjective, because every 1-dimensional complex subspace contains elements of unit norm.
\end{defn}
There are many additional properties for such construction, we will check them just for curiosity.
We need to show that it is a submersion.
\begin{proof}
Let $z_0\in S^{2n+1}$ and set $\zeta_0=p(z_0)\in \C P^n$. Since $\pi$ is a smooth submersion, it has a smooth local section $\sigma: U\to \C^{n+1}$ defined on a neighborhood $U$ of $\zeta_0$ and satisfying $\sigma(\zeta_0)=z_0$ by the local section theorem (Theorem \ref{theorem:local_section_theorem}). Let $v:\C^{n+1}\setminus\{0\}\to S^{2n+1}$ be the radial projection on to the sphere:
$$
v(z)=\frac{z}{|z|}
$$
Since dividing an element of $\C^{n+1}$ by a nonzero scalar does not change its span, it follows that $p\circ v=\pi$. Therefore, if we set $\tilde{\sigma}=v\circ \sigma$, then $\tilde{\sigma}$ is a smooth local section of $p$. Apply the local section theorem (Theorem \ref{theorem:local_section_theorem}) again, this shows that $p$ is a submersion.
Define an action of $S^1$ on $S^{2n+1}$ by complex multiplication:
$$
\lambda (z^1,z^2,\ldots,z^{n+1})=(\lambda z^1,\lambda z^2,\ldots,\lambda z^{n+1})
$$
for $\lambda\in S^1$ (viewed as complex number of norm 1) and $z=(z^1,z^2,\ldots,z^{n+1})\in S^{2n+1}$. This is easily seen to be isometric, vertical, and transitive on fibers of $p$.
By (Theorem \ref{theorem:riemannian-submersion}). Therefore, there is a unique metric on $\C P^n$ such that the map $p:S^{2n+1}\to \C P^n$ is a Riemannian submersion. This metric is called the Fubini-study metric.
\end{proof}
\subsection{Observable diameter for complex projective spaces}
Using the projection map and Hopf's fibration, we can estimate the observable diameter for complex projective spaces from the observable diameter of spheres.
\begin{theorem}
\label{thm:observable-diameter-complex-projective-space}
For any real number $\kappa$ with $0<\kappa<1$, we have
$$
\obdiam(\mathbb{C}P^n(1);-\kappa)\leq O(\sqrt{n})
$$
\end{theorem}
\begin{proof}
Recall from Example 2.30 in \cite{lee_introduction_2018}, the Hopf fibration $f_n:S^{2n+1}(1)\to \C P^n$ is 1-Lipschitz continuous with respect to the Fubini-Study metric on $\C P^n$. and the push-forward $(f_n)_*\sigma^{2n+1}$ coincides with the normalized volume measure on $\C P^n$ induced from the Fubini-Study metric.
By proposition \ref{prop:observable-diameter-domination}, we have $\obdiam(\mathbb{C}P^n(1);-\kappa)\leq \obdiam(S^{2n+1}(1);-\kappa)\leq O(\sqrt{n})$.
\end{proof}
\section{Use entropy function as estimator of observable diameter for complex projective spaces}
In this section, we wish to use observable diameter to estimate the statics of thermal dynamics of some classical systems.
\ifSubfilesClassLoaded{
\printbibliography[title={References}]
}
\end{document}
% chapters/chap2.tex
\documentclass[../main.tex]{subfiles}
\ifSubfilesClassLoaded{
\addbibresource{../main.bib}
}
\begin{document}
\chapter{Levy's family and observable diameters}
In this section, we will explore how the results from Hayden's concentration of measure theorem can be understood in terms of observable diameters from Gromov's perspective and what properties it reveals for entropy functions.
We will try to use the results from previous sections to estimate the observable diameter for complex projective spaces.
\section{Observable diameters}
Recall from previous sections, an arbitrary 1-Lipschitz function $f:S^n\to \mathbb{R}$ concentrates near a single value $a_0\in \mathbb{R}$ as strongly as the distance function does.
\begin{defn}
\label{defn:mm-space}
Let $X$ be a topological space with the following:
\begin{enumerate}
\item $X$ is a complete (every Cauchy sequence converges)
\item $X$ is a metric space with metric $d_X$
\item $X$ has a Borel probability measure $\mu_X$
\end{enumerate}
Then $(X,d_X,\mu_X)$ is a \textbf{metric measure space}.
\end{defn}
\begin{defn}
\label{defn:diameter}
Let $(X,d_X)$ be a metric space. The \textbf{diameter} of a set $A\subset X$ is defined as
$$
\diam(A)=\sup_{x,y\in A}d_X(x,y).
$$
\end{defn}
\begin{defn}
\label{defn:partial-diameter}
Let $(X,d_X,\mu_X)$ be a metric measure space, For any real number $\alpha\leq 1$, the \textbf{partial diameter} of $X$ is defined as
$$
\diam(A;\alpha)=\inf_{A\subseteq X|\mu_X(A)\geq \alpha}\diam(A).
$$
\end{defn}
This definition generalize the relation between the measure and metric in the metric-measure space. Intuitively, the space with smaller partial diameter can take more volume with the same diameter constrains.
However, in higher dimensions, the volume may tend to concentrates more around a small neighborhood of the set, as we see in previous chapters with high dimensional sphere as example. We can safely cut $\kappa>0$ volume to significantly reduce the diameter, this yields better measure for concentration for shapes in spaces with high dimension.
\begin{defn}
\label{defn:observable-diameter}
Let $X$ be a metric-measure space, $Y$ be a metric space, and $f:X\to Y$ be a 1-Lipschitz function. Then $f_*\mu_X=\mu_Y$ is a push forward measure on $Y$.
For any real number $\kappa>0$, the \textbf{$\kappa$-observable diameter with screen $Y$} is defined as
$$
\obdiam_Y(X;\kappa)=\sup\{\diam(f_*\mu_X;1-\kappa)\}
$$
And the \textbf{obbservable diameter with screen $Y$} is defined as
$$
\obdiam_Y(X)=\inf_{\kappa>0}\max\{\obdiam_Y(X;\kappa)\}
$$
If $Y=\R$, we call it the \textbf{observable diameter}.
\end{defn}
If we collapse it naively via
$$
\inf_{\kappa>0}\obdiam_Y(X;\kappa),
$$
we typically get something degenerate: as $\kappa\to 1$, the condition ``mass $\ge 1-\kappa$'' becomes almost empty space, so $\diam(\nu;1-\kappa)$ can be forced to be $0$ (take a tiny set of positive mass), and hence the infimum tends to $0$ for essentially any non-atomic space.
This is why one either:
\begin{enumerate}
\item keeps $\obdiam_Y(X;\kappa)$ as a \emph{function of $\kappa$} (picking $\kappa$ to be small but not $0$), or
\item if one insists on a single number, balances ``spread'' against ``exceptional mass'' by defining $\obdiam_Y(X)=\inf_{\kappa>0}\max\{\obdiam_Y(X;\kappa),\kappa\}$ as above.
\end{enumerate}
The point of the $\max\{\cdot,\kappa\}$ is that it prevents cheating by taking $\kappa$ close to $1$: if $\kappa$ is large then the maximum is large regardless of how small $\obdiam_Y(X;\kappa)$ is, so the infimum is forced to occur where the exceptional mass and the observable spread are small.
Few additional proposition in \cite{shioya2014metricmeasuregeometry} will help us to estimate the observable diameter for complex projective spaces.
\begin{prop}
\label{prop:observable-diameter-domination}
Let $X$ and $Y$ be two metric-measure spaces and $\kappa>0$, and let $f:Y\to X$ be a 1-Lipschitz function ($Y$ dominates $X$, denoted as $X\prec Y$) then:
\begin{enumerate}
\item
$
\diam(X,1-\kappa)\leq \diam(Y,1-\kappa)
$
\item $\obdiam(X;-\kappa)\leq \diam(X;1-\kappa)$, and $\obdiam(X)$ is finite.
\item
$
\obdiam(X;-\kappa)\leq \obdiam(Y;-\kappa)
$
\end{enumerate}
\end{prop}
\begin{proof}
Since $f$ is 1-Lipschitz, we have $f_*\mu Y=\mu_X$. Let $A$ be any Borel set of $Y$ with $\mu_Y(A)\geq 1-\kappa$ and $\overline{f(A)}$ be the closure of $f(A)$ in $X$. We have $\mu_X(\overline{f(A)})=\mu_Y(f^{-1}(\overline{f(A)}))\geq \mu_Y(A)\geq 1-\kappa$ and by the 1-lipschitz property, $\diam(\overline{f(A)})\leq \diam(A)$, so $\diam(X;1-\kappa)\leq \diam(A)\leq \diam(Y;1-\kappa)$.
Let $g:X\to \R$ be any 1-lipschitz function, since $(\R,|\cdot|,g_*\mu_X)$ is dominated by $X$, $\diam(\R;1-\kappa)\leq \diam(X;1-\kappa)$. Therefore, $\obdiam(X;-\kappa)\leq \diam(X;1-\kappa)$.
and
$$
\diam(g_*\mu_X;1-\kappa)\leq \diam((f\circ g)_*\mu_Y;1-\kappa)\leq \obdiam(Y;1-\kappa)
$$
\end{proof}
\begin{prop}
\label{prop:observable-diameter-scale}
Let $X$ be an metric-measure space. Then for any real number $t>0$, we have
$$
\obdiam(tX;-\kappa)=t\obdiam(X;-\kappa)
$$
Where $tX=(X,tdX,\mu X)$.
\end{prop}
\begin{proof}
$$
\begin{aligned}
\obdiam(tX;-\kappa)&=\sup\{\diam(f_*\mu_X;1-\kappa)|f:tX\to \R \text{ is 1-Lipschitz}\}\\
&=\sup\{\diam(f_*\mu_X;1-\kappa)|t^{-1}f:X\to \R \text{ is 1-Lipschitz}\}\\
&=\sup\{\diam((tg)_*\mu_X;1-\kappa)|g:X\to \R \text{ is 1-Lipschitz}\}\\
&=t\sup\{\diam(g_*\mu_X;1-\kappa)|g:X\to \R \text{ is 1-Lipschitz}\}\\
&=t\obdiam(X;-\kappa)
\end{aligned}
$$
\end{proof}
\subsection{Observable diameter for class of spheres}
In this section, we will try to use the results from previous sections to estimate the observable diameter for class of spheres.
\begin{theorem}
\label{thm:observable-diameter-sphere}
For any real number $\kappa$ with $0<\kappa<1$, we have
$$
\obdiam(S^n(1);-\kappa)=O(\sqrt{n})
$$
\end{theorem}
\begin{proof}
First, recall that by maxwell boltzmann distribution, we have that for any $n>0$, let $I(r)$ denote the measure of standard gaussian measure on the interval $[0,r]$. Then we have
$$
\begin{aligned}
\lim_{n\to \infty} \obdiam(S^n(\sqrt{n});-\kappa)&=\lim_{n\to \infty} \sup\{\diam((\pi_{n,k})_*\sigma^n;1-\kappa)|\pi_{n,k} \text{ is 1-Lipschitz}\}\\
&=\lim_{n\to \infty} \sup\{\diam(\gamma^1;1-\kappa)|\gamma^1 \text{ is the standard gaussian measure}\}\\
&=\diam(\gamma^1;1-\kappa)\\
&=2I^{-1}(\frac{1-\kappa}{2})\text { cutting the extremum for normal distribution}\\
\end{aligned}
$$
By proposition \ref{prop:observable-diameter-scale}, we have
$$
\obdiam(S^n(\sqrt{n});-\kappa)=\sqrt{n}\obdiam(S^n(1);-\kappa)
$$
So $\obdiam(S^n(1);-\kappa)=\sqrt{n}(2I^{-1}(\frac{1-\kappa}{2}))=O(\sqrt{n})$.
\end{proof}
From the previous discussion, we see that the only remaining for finding observable diameter of $\C P^n$ is to find the lipchitz function that is isometric with consistent push-forward measure.
To find such metric, we need some additional results.
\begin{defn}
\label{defn:riemannian-metric}
Let $M$ be a smooth manifold. A \textit{\textbf{Riemannian metric}} on $M$ is a smooth covariant tensor field $g\in \mathcal{T}^2(M)$ such that for each $p\in M$, $g_p$ is an inner product on $T_pM$.
$g_p(v,v)\geq 0$ for each $p\in M$ and each $v\in T_pM$. equality holds if and only if $v=0$.
\end{defn}
% TODO: There is a hidden chapter on group action on manifolds, can you find that?
\begin{theorem}
\label{theorem:riemannian-submersion}
Let $(\tilde{M},\tilde{g})$ be a Riemannian manifold, let $\pi:\tilde{M}\to M$ be a surjective smooth submersion, and let $G$ be a group acting on $\tilde{M}$. If the \textbf{action} is
\begin{enumerate}
\item isometric: the map $x\mapsto \varphi\cdot x$ is an isometry for each $\varphi\in G$.
\item vertical: every element $\varphi\in G$ takes each fiber to itself, that is $\pi(\varphi\cdot p)=\pi(p)$ for all $p\in \tilde{M}$.
\item transitive on fibers: for each $p,q\in \tilde{M}$ such that $\pi(p)=\pi(q)$, there exists $\varphi\in G$ such that $\varphi\cdot p = q$.
\end{enumerate}
Then there is a unique Riemannian metric on $M$ such that $\pi$ is a Riemannian submersion.
\end{theorem}
A natural measure for $\C P^n$ is the normalized volume measure on $\C P^n$ induced from the Fubini-Study metric. \cite{lee_introduction_2018} Example 2.30
\begin{defn}
\label{defn:fubini-study-metric}
Let $n$ be a positive integer, and consider the complex projective space $\C P^n$ defined as the quotient space of $\C^{n+1}$ by the equivalence relation $z\sim z'$ if there exists $\lambda \in \C$ such that $z=\lambda z'$. The map $\pi:\C^{n+1}\setminus\{0\}\to \C P^n$ sending each point in $\C^{n+1}\setminus\{0\}$ to its span is surjective smooth submersion.
Identifying $\C^{n+1}$ with $\R^{2n+2}$ with its Euclidean metric, we can view the unit sphere $S^{2n+1}$ with its roud metric $\mathring{g}$ as an embedded Riemannian submanifold of $\C^{n+1}\setminus\{0\}$. Let $p:S^{2n+1}\to \C P^n$ denote the restriction of the map $\pi$. Then $p$ is smooth, and its is surjective, because every 1-dimensional complex subspace contains elements of unit norm.
\end{defn}
There are many additional properties for such construction, we will check them just for curiosity.
We need to show that it is a submersion.
\begin{proof}
Let $z_0\in S^{2n+1}$ and set $\zeta_0=p(z_0)\in \C P^n$. Since $\pi$ is a smooth submersion, it has a smooth local section $\sigma: U\to \C^{n+1}$ defined on a neighborhood $U$ of $\zeta_0$ and satisfying $\sigma(\zeta_0)=z_0$ by the local section theorem (Theorem \ref{theorem:local_section_theorem}). Let $v:\C^{n+1}\setminus\{0\}\to S^{2n+1}$ be the radial projection on to the sphere:
$$
v(z)=\frac{z}{|z|}
$$
Since dividing an element of $\C^{n+1}$ by a nonzero scalar does not change its span, it follows that $p\circ v=\pi$. Therefore, if we set $\tilde{\sigma}=v\circ \sigma$, then $\tilde{\sigma}$ is a smooth local section of $p$. Apply the local section theorem (Theorem \ref{theorem:local_section_theorem}) again, this shows that $p$ is a submersion.
Define an action of $S^1$ on $S^{2n+1}$ by complex multiplication:
$$
\lambda (z^1,z^2,\ldots,z^{n+1})=(\lambda z^1,\lambda z^2,\ldots,\lambda z^{n+1})
$$
for $\lambda\in S^1$ (viewed as complex number of norm 1) and $z=(z^1,z^2,\ldots,z^{n+1})\in S^{2n+1}$. This is easily seen to be isometric, vertical, and transitive on fibers of $p$.
By (Theorem \ref{theorem:riemannian-submersion}). Therefore, there is a unique metric on $\C P^n$ such that the map $p:S^{2n+1}\to \C P^n$ is a Riemannian submersion. This metric is called the Fubini-study metric.
\end{proof}
\subsection{Observable diameter for complex projective spaces}
Using the projection map and Hopf's fibration, we can estimate the observable diameter for complex projective spaces from the observable diameter of spheres.
\begin{theorem}
\label{thm:observable-diameter-complex-projective-space}
For any real number $\kappa$ with $0<\kappa<1$, we have
$$
\obdiam(\mathbb{C}P^n(1);-\kappa)\leq O(\sqrt{n})
$$
\end{theorem}
\begin{proof}
Recall from Example 2.30 in \cite{lee_introduction_2018}, the Hopf fibration $f_n:S^{2n+1}(1)\to \C P^n$ is 1-Lipschitz continuous with respect to the Fubini-Study metric on $\C P^n$. and the push-forward $(f_n)_*\sigma^{2n+1}$ coincides with the normalized volume measure on $\C P^n$ induced from the Fubini-Study metric.
By proposition \ref{prop:observable-diameter-domination}, we have $\obdiam(\mathbb{C}P^n(1);-\kappa)\leq \obdiam(S^{2n+1}(1);-\kappa)\leq O(\sqrt{n})$.
\end{proof}
\section{Use entropy function as estimator of observable diameter for complex projective spaces}
In this section we describe a Monte Carlo pipeline for comparing concentration phenomena across three metric-measure spaces using real-valued entropy observables. The goal is not to compute the exact observable diameter
$$
\operatorname{ObsDiam}_{\mathbb{R}}(X;-\kappa)
=
\sup_{f \in \operatorname{Lip}_1(X,\mathbb{R})}
\operatorname{diam}(f_*\mu_X;1-\kappa),
$$
but to estimate it by choosing a specific observable $f:X\to \mathbb{R}$ and then measuring the partial diameter of its push-forward distribution. Thus all numerical quantities below should be interpreted as \emph{entropy-based observable-diameter proxies}, not exact observable diameters in Gromov's sense \cite{MGomolovs,shioya2014metricmeasuregeometry}.
The screen is $\mathbb{R}$ equipped with the Euclidean metric, and for a fixed $\kappa \in (0,1)$ we set
$$
\alpha = 1-\kappa.
$$
Given sampled values $y_1,\dots,y_N \in \mathbb{R}$ of the observable, the code sorts them and computes the shortest interval $[a,b]$ containing at least $\lceil \alpha N \rceil$ samples. Its width
$$
b-a
$$
is the empirical partial diameter of the push-forward measure on $\mathbb{R}$.
To compare this width with the true observable diameter, the code also estimates an empirical Lipschitz constant of the chosen observable. If $x_i,x_j \in X$ are sampled states and $f(x_i),f(x_j)$ are the corresponding observable values, then the sampled slopes are
$$
\frac{|f(x_i)-f(x_j)|}{d_X(x_i,x_j)},
$$
where $d_X$ is the metric of the ambient space. The code records both the maximum sampled slope and the $0.99$-quantile of these slopes. Dividing the empirical partial diameter by these sampled Lipschitz constants gives two normalized proxies:
$$
\frac{\operatorname{diam}(f_*\mu_X;1-\kappa)}{L_{\max}}
\qquad \text{and} \qquad
\frac{\operatorname{diam}(f_*\mu_X;1-\kappa)}{L_{0.99}}.
$$
If the chosen observable were exactly $1$-Lipschitz, these normalized quantities would coincide with the raw width. In practice they should be viewed only as heuristic lower-scale corrections.
\subsection{Random sampling using standard uniform measure on the unit sphere}
The first family of spaces is the real unit sphere
$$
S^{m-1}
=
\left\{
x=(x_1,\dots,x_m)\in \mathbb{R}^m : \|x\|_2=1
\right\},
$$
equipped with the geodesic distance
$$
d_{S}(x,y)=\arccos \langle x,y\rangle
$$
and the normalized Riemannian volume measure. This is the standard metric-measure structure used in concentration of measure on spheres \cite{lee_introduction_2018,romanvershyni,shioya2014metricmeasuregeometry}.
Sampling is performed by drawing a standard Gaussian vector $g\in \mathbb{R}^m$ and normalizing:
$$
x=\frac{g}{\|g\|_2}.
$$
This produces the uniform distribution on $S^{m-1}$.
The observable is a Shannon entropy built from the squared coordinates:
$$
f_{\mathrm{sphere}}(x)
=
-\sum_{i=1}^m x_i^2 \log_2(x_i^2).
$$
Since $(x_1^2,\dots,x_m^2)$ is a probability vector, $f_{\mathrm{sphere}}$ takes values in $[0,\log_2 m]$, and the code records $\log_2 m$ as the natural upper bound of the observable.
For each chosen dimension $m$, the experiment generates $N$ independent samples $x^{(1)},\dots,x^{(N)}$, computes the values
$$
f_{\mathrm{sphere}}(x^{(1)}),\dots,f_{\mathrm{sphere}}(x^{(N)}),
$$
and then evaluates the shortest interval containing mass at least $1-\kappa$. This gives an empirical observable-diameter proxy for the sphere family. The code also computes the empirical mean, median, standard deviation, and the normalized proxies obtained from sampled Lipschitz ratios.
\subsection{Visualized the concentration of measure phenomenon on complex projective space}
The second family is complex projective space
$$
\mathbb{C}P^{d_A d_B-1},
$$
viewed as the space of pure states in $\mathbb{C}^{d_A}\otimes \mathbb{C}^{d_B}$ modulo global phase. Geometrically, this space is equipped with the Fubini--Study metric and its associated normalized volume measure \cite{lee_introduction_2018,Bengtsson_Zyczkowski_2017}. Numerically, a projective point is represented by a unit vector
$$
\psi \in \mathbb{C}^{d_A d_B},
\qquad
\|\psi\|=1,
$$
and distances are computed by the Fubini--Study formula
$$
d_{FS}([\psi],[\phi])
=
\arccos |\langle \psi,\phi\rangle|.
$$
Sampling is implemented by drawing a complex Gaussian matrix
$$
G \in \mathbb{C}^{d_A \times d_B},
$$
with independent standard complex normal entries, and then normalizing it so that
$$
\psi = \frac{\operatorname{vec}(G)}{\|\operatorname{vec}(G)\|}.
$$
This is equivalent to Haar sampling on the unit sphere in $\mathbb{C}^{d_A d_B}$ and hence induces the standard unitarily invariant measure on $\mathbb{C}P^{d_A d_B-1}$ \cite{Bengtsson_Zyczkowski_2017,Nielsen_Chuang_2010}.
The real-valued observable is the bipartite entanglement entropy. Writing
$$
\rho_A = \operatorname{Tr}_B |\psi\rangle\langle \psi|,
$$
the code defines
$$
f_{\mathrm{CP}}([\psi])
=
S(\rho_A)
=
-\operatorname{Tr}(\rho_A \log_2 \rho_A).
$$
Equivalently, if $\lambda_1,\dots,\lambda_{d_A}$ are the eigenvalues of $\rho_A$, then
$$
f_{\mathrm{CP}}([\psi])
=
-\sum_{i=1}^{d_A}\lambda_i \log_2 \lambda_i.
$$
This observable takes values in $[0,\log_2 d_A]$.
For each dimension pair $(d_A,d_B)$, the experiment samples $N$ independent Haar-random pure states, computes the entropy values, and then forms the empirical push-forward distribution on $\mathbb{R}$. The shortest interval containing mass at least $1-\kappa$ is reported as the entropy-based observable-diameter proxy. In addition, the code plots histograms, upper-tail deficit plots for
$$
\log_2 d_A - S(\rho_A),
$$
and family-wise comparisons of partial diameter, standard deviation, and mean deficit. When available, these plots are overlaid with the Page average entropy and with Hayden-style concentration scales, which serve as theoretical guides rather than direct outputs of the simulation \cite{Hayden,Hayden_2006,Pages_conjecture_simple_proof}.
\subsection{Random sampling using Majorana Stellar representation}
The third family is the symmetric subspace
$$
\operatorname{Sym}^N(\mathbb{C}^2),
$$
which is naturally identified with $\mathbb{C}P^N$ after projectivization. In this model, a pure symmetric $N$-qubit state is written in the Dicke basis as
$$
|\psi\rangle
=
\sum_{k=0}^{N} c_k |D^N_k\rangle,
\qquad
\sum_{k=0}^{N}|c_k|^2 = 1.
$$
The projective metric is again the Fubini--Study metric
$$
d_{FS}([\psi],[\phi])=\arccos |\langle \psi,\phi\rangle|.
$$
Sampling is performed by drawing a standard complex Gaussian vector
$$
(c_0,\dots,c_N)\in \mathbb{C}^{N+1}
$$
and normalizing it. This gives the unitarily invariant measure on the projective symmetric state space.
The observable used by the code is the one-particle entropy of the symmetric state. From the coefficient vector $(c_0,\dots,c_N)$ one constructs the one-qubit reduced density matrix $\rho_1$, and then defines
$$
f_{\mathrm{Maj}}([\psi])
=
S(\rho_1)
=
-\operatorname{Tr}(\rho_1 \log_2 \rho_1).
$$
Since $\rho_1$ is a qubit state, this observable takes values in $[0,1]$.
To visualize the same states in Majorana form, the code also associates to a sampled symmetric state its Majorana polynomial and computes its roots. After stereographic projection, these roots define $N$ points on $S^2$, called the Majorana stars \cite{Bengtsson_Zyczkowski_2017}. The resulting star plots are included only as geometric visualizations; they are not used to define the metric or the observable. The metric-measure structure used in the actual simulation remains the Fubini--Study metric and the unitarily invariant measure on the projective symmetric state space.
Thus, for each $N$, the simulation produces:
\begin{enumerate}
\item a sample of symmetric states,
\item the corresponding one-body entropy values,
\item the shortest interval containing mass at least $1-\kappa$ in the push-forward distribution on $\mathbb{R}$,
\item empirical Lipschitz-normalized versions of this width,
\item and a separate Majorana-star visualization of representative samples.
\end{enumerate}
Taken together, these three families allow us to compare how entropy-based concentration behaves on a real sphere, on a general complex projective space carrying bipartite entanglement entropy, and on the symmetric subspace described by Majorana stellar data.
\ifSubfilesClassLoaded{
\printbibliography[title={References}]
}
\end{document}

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@@ -1,57 +1,57 @@
% chapters/chap2.tex
\documentclass[../main.tex]{subfiles}
\ifSubfilesClassLoaded{
\addbibresource{../main.bib}
}
\begin{document}
\chapter{Seigel-Bargmann Space}
In this chapter, we will collect ideas and other perspective we have understanding the concentration of measure phenomenon. Especially with symmetric product of $\C P^1$ and see how it relates to Riemman surfaces and Seigel-Bargmann spaces.
\begin{figure}[h]
\centering
\begin{tikzpicture}[node distance=40mm, thick,
main/.style={draw, draw=white},
towards/.style={->},
towards_imp/.style={<->,red},
mutual/.style={<->}
]
\node[main] (cp) {$\mathbb{C}P^{n}$};
\node[main] (c) [below of=cp] {$\mathbb{C}^{n+1}$};
\node[main] (p) [right of=cp] {$\mathbb{P}^n$};
\node[main] (sym) [below of=p] {$\operatorname{Sym}_n(\mathbb{C}P^1)$};
% draw edges
\draw[towards] (c) -- (cp) node[midway, left] {$z\sim \lambda z$};
\draw[towards] (c) -- (p) node[midway, fill=white] {$w(z)=\sum_{i=0}^n Z_i z^i$};
\draw[towards_imp] (cp) -- (p) node[midway, above] {$w(z)\sim w(\lambda z)$};
\draw[mutual] (p) -- (sym) node[midway, right] {root of $w(z)$};
\end{tikzpicture}
\caption{Majorana stellar representation}
\label{fig:majorana_stellar_representation}
\end{figure}
Basically, there is a bijection between the complex projective space $\mathbb{C}P^n$ and the set of roots of a polynomial of degree $n$.
We can use a symmetric group of permutations of $n$ complex numbers (or $S^2$) to represent the $\mathbb{C}P^n$, that is, $\mathbb{C}P^n=S^2\times S^2\times \cdots \times S^2/S_n$.
One might be interested in the random sampling over the $\operatorname{Sym}_n(\mathbb{C}P^1)$ and the concentration of measure phenomenon on that.
\section{Majorana stellar representation of the quantum state}
\begin{defn}
Let $n$ be a positive integer. The Majorana stellar representation of the quantum state is the set of all roots of a polynomial of degree $n$ in $\mathbb{C}$.
\end{defn}
\section{Space of complex valued functions and pure states}
\ifSubfilesClassLoaded{
\printbibliography[title={References for Chapter 2}]
}
\end{document}
% chapters/chap2.tex
\documentclass[../main.tex]{subfiles}
\ifSubfilesClassLoaded{
\addbibresource{../main.bib}
}
\begin{document}
\chapter{Seigel-Bargmann Space}
In this chapter, we will collect ideas and other perspective we have understanding the concentration of measure phenomenon. Especially with symmetric product of $\C P^1$ and see how it relates to Riemman surfaces and Seigel-Bargmann spaces.
\begin{figure}[h]
\centering
\begin{tikzpicture}[node distance=40mm, thick,
main/.style={draw, draw=white},
towards/.style={->},
towards_imp/.style={<->,red},
mutual/.style={<->}
]
\node[main] (cp) {$\mathbb{C}P^{n}$};
\node[main] (c) [below of=cp] {$\mathbb{C}^{n+1}$};
\node[main] (p) [right of=cp] {$\mathbb{P}^n$};
\node[main] (sym) [below of=p] {$\operatorname{Sym}_n(\mathbb{C}P^1)$};
% draw edges
\draw[towards] (c) -- (cp) node[midway, left] {$z\sim \lambda z$};
\draw[towards] (c) -- (p) node[midway, fill=white] {$w(z)=\sum_{i=0}^n Z_i z^i$};
\draw[towards_imp] (cp) -- (p) node[midway, above] {$w(z)\sim w(\lambda z)$};
\draw[mutual] (p) -- (sym) node[midway, right] {root of $w(z)$};
\end{tikzpicture}
\caption{Majorana stellar representation}
\label{fig:majorana_stellar_representation}
\end{figure}
Basically, there is a bijection between the complex projective space $\mathbb{C}P^n$ and the set of roots of a polynomial of degree $n$.
We can use a symmetric group of permutations of $n$ complex numbers (or $S^2$) to represent the $\mathbb{C}P^n$, that is, $\mathbb{C}P^n=S^2\times S^2\times \cdots \times S^2/S_n$.
One might be interested in the random sampling over the $\operatorname{Sym}_n(\mathbb{C}P^1)$ and the concentration of measure phenomenon on that.
\section{Majorana stellar representation of the quantum state}
\begin{defn}
Let $n$ be a positive integer. The Majorana stellar representation of the quantum state is the set of all roots of a polynomial of degree $n$ in $\mathbb{C}$.
\end{defn}
\section{Space of complex valued functions and pure states}
\ifSubfilesClassLoaded{
\printbibliography[title={References for Chapter 2}]
}
\end{document}

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@@ -1,112 +1,112 @@
% main.tex
\documentclass[11pt]{book}
% --- Math + structure ---
\usepackage{amsmath,amssymb,amsthm}
\usepackage{hyperref}
\usepackage{subfiles} % allows chapters to compile independently
% --- Formatting ---
\usepackage{fancyhdr,parskip}
\usepackage{fullpage}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% add special notation supports
\usepackage[mathscr]{euscript}
\usepackage{mathtools}
\usepackage{braket}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% add image package and directory
\usepackage{graphicx}
\usepackage{tikz}
\graphicspath{{./images/}}
% dependency graph
\usetikzlibrary{trees,positioning,arrows.meta,backgrounds}
% floating graph
\usepackage{float}
% --- Bibliography: biblatex + biber ---
\usepackage[
backend=biber,
style=alphabetic,
sorting=nyt,
giveninits=true
]{biblatex}
% --- Beamer-like blocks (printer-friendly) ---
\usepackage[most]{tcolorbox}
\usepackage{xcolor}
% A dedicated "Examples" block (optional convenience wrapper)
\newtcolorbox{examples}[1][Example]{%
enhanced,
breakable,
colback=white,
colframe=black!90,
coltitle=white, % title text color
colbacktitle=black!90, % <<< grey 80 title bar
boxrule=0.6pt,
arc=1.5mm,
left=1.2mm,right=1.2mm,top=1.0mm,bottom=1.0mm,
fonttitle=\bfseries,
title=#1
}
% In the assembled book, we load *all* chapter bib files here,
% and print one combined bibliography at the end.
\addbibresource{main.bib}
%%
% Some convenient commands if you need to use integrals
\newcommand{\is}{\hspace{2pt}}
\newcommand{\dx}{\is dx}
%%%%%%%%%%%%%%%%%%%%%%
% These are commands you can use that will generate nice things in TeX. Feel free to define your own, too.
\newcommand{\Z}{\mathbb{Z}} % integers
\newcommand{\Q}{\mathbb{Q}} % rationals
\newcommand{\R}{\mathbb{R}} % reals
\newcommand{\C}{\mathbb{C}} % complex numbers
\newcommand{\ds}{\displaystyle} % invoke "display style", which makes fractions come out big, etc.
\newcommand{\charac}{\operatorname{char}} % characteristic of a field
\newcommand{\st}{\ensuremath{\,:\,}} % Makes the colon in set-builder notation space properly
%%%%%%%%%%%%%%%%%%%%%%
% These commands are for convenient notation for the concentration of measure theorem
\newcommand{\obdiam}{\operatorname{ObserDiam}}
\newcommand{\diam}{\operatorname{diam}}
%%%%%%%%%%%%%%%%%%%%%%
% These commands create theorem-like environments.
\newtheorem{theorem}{Theorem}
\newtheorem{lemma}[theorem]{Lemma}
\newtheorem{prop}[theorem]{Proposition}
\newtheorem{defn}[theorem]{Definition}
\title{Concentration of Measure And Quantum Entanglement}
\author{Zheyuan Wu}
\date{\today}
\begin{document}
\frontmatter
\maketitle
\tableofcontents
\mainmatter
% Each chapter is in its own file and included as a subfile.
% \subfile{preface}
\subfile{chapters/chap0}
\subfile{chapters/chap1}
\subfile{chapters/chap2}
% \subfile{chapters/chap3}
\backmatter
\cleardoublepage
\printbibliography[title={References}]
\end{document}
% main.tex
\documentclass[11pt]{book}
% --- Math + structure ---
\usepackage{amsmath,amssymb,amsthm}
\usepackage{hyperref}
\usepackage{subfiles} % allows chapters to compile independently
% --- Formatting ---
\usepackage{fancyhdr,parskip}
\usepackage{fullpage}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% add special notation supports
\usepackage[mathscr]{euscript}
\usepackage{mathtools}
\usepackage{braket}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% add image package and directory
\usepackage{graphicx}
\usepackage{tikz}
\graphicspath{{./images/}}
% dependency graph
\usetikzlibrary{trees,positioning,arrows.meta,backgrounds}
% floating graph
\usepackage{float}
% --- Bibliography: biblatex + biber ---
\usepackage[
backend=biber,
style=alphabetic,
sorting=nyt,
giveninits=true
]{biblatex}
% --- Beamer-like blocks (printer-friendly) ---
\usepackage[most]{tcolorbox}
\usepackage{xcolor}
% A dedicated "Examples" block (optional convenience wrapper)
\newtcolorbox{examples}[1][Example]{%
enhanced,
breakable,
colback=white,
colframe=black!90,
coltitle=white, % title text color
colbacktitle=black!90, % <<< grey 80 title bar
boxrule=0.6pt,
arc=1.5mm,
left=1.2mm,right=1.2mm,top=1.0mm,bottom=1.0mm,
fonttitle=\bfseries,
title=#1
}
% In the assembled book, we load *all* chapter bib files here,
% and print one combined bibliography at the end.
\addbibresource{main.bib}
%%
% Some convenient commands if you need to use integrals
\newcommand{\is}{\hspace{2pt}}
\newcommand{\dx}{\is dx}
%%%%%%%%%%%%%%%%%%%%%%
% These are commands you can use that will generate nice things in TeX. Feel free to define your own, too.
\newcommand{\Z}{\mathbb{Z}} % integers
\newcommand{\Q}{\mathbb{Q}} % rationals
\newcommand{\R}{\mathbb{R}} % reals
\newcommand{\C}{\mathbb{C}} % complex numbers
\newcommand{\ds}{\displaystyle} % invoke "display style", which makes fractions come out big, etc.
\newcommand{\charac}{\operatorname{char}} % characteristic of a field
\newcommand{\st}{\ensuremath{\,:\,}} % Makes the colon in set-builder notation space properly
%%%%%%%%%%%%%%%%%%%%%%
% These commands are for convenient notation for the concentration of measure theorem
\newcommand{\obdiam}{\operatorname{ObserDiam}}
\newcommand{\diam}{\operatorname{diam}}
%%%%%%%%%%%%%%%%%%%%%%
% These commands create theorem-like environments.
\newtheorem{theorem}{Theorem}
\newtheorem{lemma}[theorem]{Lemma}
\newtheorem{prop}[theorem]{Proposition}
\newtheorem{defn}[theorem]{Definition}
\title{Concentration of Measure And Quantum Entanglement}
\author{Zheyuan Wu}
\date{\today}
\begin{document}
\frontmatter
\maketitle
\tableofcontents
\mainmatter
% Each chapter is in its own file and included as a subfile.
% \subfile{preface}
\subfile{chapters/chap0}
\subfile{chapters/chap1}
\subfile{chapters/chap2}
% \subfile{chapters/chap3}
\backmatter
\cleardoublepage
\printbibliography[title={References}]
\end{document}

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@@ -1,86 +1,86 @@
% preface.tex
\documentclass[main.tex]{subfiles}
\ifSubfilesClassLoaded{
\addbibresource{main.bib}
}
\begin{document}
\chapter*{Preface}
\addcontentsline{toc}{chapter}{Preface}
Non-commutative probability theory is a branch of generalized probability theory that studies the probability of events in non-commutative algebras (e.g. the algebra of observables in quantum mechanics). In the 20th century, non-commutative probability theory has been applied to the study of quantum mechanics as the classical probability theory is not enough to describe quantum mechanics~\cite{kummer1998elements}.
Recently, the concentration of measure phenomenon has been applied to the study of non-commutative probability theory. Basically, the non-trivial observation, citing from Gromov's work~\cite{MGomolovs}, states that an arbitrary 1-Lipschitz function $f:S^n\to \mathbb{R}$ concentrates near a single value $a_0\in \mathbb{R}$ as strongly as the distance function does. That is,
$$
\mu\{x\in S^n: |f(x)-a_0|\geq\epsilon\} < \kappa_n(\epsilon)\leq 2\exp\left(-\frac{(n-1)\epsilon^2}{2}\right)
$$
is applied to computing the probability that, given a bipartite system $A\otimes B$, assume $\dim(B)\geq \dim(A)\geq 3$, as the dimension of the smaller system $A$ increases, with very high probability, a random pure state $\sigma=|\psi\rangle\langle\psi|$ selected from $A\otimes B$ is almost as good as the maximally entangled state.
Mathematically, that is:
Let $\psi\in \mathcal{P}(A\otimes B)$ be a random pure state on $A\otimes B$.
If we define $\beta=\frac{1}{\ln(2)}\frac{d_A}{d_B}$, then we have
$$
\operatorname{Pr}[H(\psi_A) < \log_2(d_A)-\alpha-\beta] \leq \exp\left(-\frac{1}{8\pi^2\ln(2)}\frac{(d_Ad_B-1)\alpha^2}{(\log_2(d_A))^2}\right)
$$
where $d_B\geq d_A\geq 3$~\cite{Hayden_2006}.
In this report, we will show the process of my exploration of the concentration of measure phenomenon in the context of non-commutative probability theory. We assume the reader is an undergraduate student in mathematics and is familiar with the basic concepts of probability theory, measure theory, linear algebra, and some basic skills of mathematical analysis. To make the report more self-contained, we will add detailed annotated proofs that I understand and references for the original sources.
\section*{How to use the dependency graph}
Since our topic integrates almost everything I've learned during undergraduate study, I will try to make some dependency graph for reader and for me to keep track of what are the necessary knowledge to understand part of the report.
One can imagine the project as a big tree, where the root is in undergrad math and branches out to the topics of the report, including many advanced topics and motivation to study them.
\bigskip
% --- Dependency tree graph (TikZ) ---
\begin{figure}[ht]
\centering
\begin{tikzpicture}[
node distance=10mm and 18mm,
box/.style={draw, rectangle, fill=white, align=center, inner sep=4pt},
arrow/.style={-Latex}
]
% \node[box] (lin) {Linear Algebra\\(bases, maps, eigenvalues)};
% \node[box, right=of lin] (real) {Real Analysis\\(limits, continuity, measure-lite)};
% \node[box, below=of lin] (prob) {Probability\\(expectation, variance, concentration)};
% \node[box, below=of real] (top) {Topology/Geometry\\(metrics, compactness)};
% \node[box, below=12mm of prob] (func) {Functional Analysis\\($L^p$, Hilbert spaces, operators)};
% \node[box, below=12mm of top] (quant) {Quantum Formalism\\(states, observables, partial trace)};
% \node[box, below=14mm of func, xshift=25mm] (book) {This Book\\(Chapters 1--n)};
% % draw arrows behind nodes
% \begin{scope}[on background layer]
% \draw[arrow] (lin) -- (func);
% \draw[arrow] (real) -- (func);
% \draw[arrow] (prob) -- (func);
% \draw[arrow] (func) -- (quant);
% \draw[arrow] (lin) -- (quant);
% \draw[arrow] (top) -- (quant);
% \draw[arrow] (func) -- (book);
% \draw[arrow] (quant) -- (book);
% \draw[arrow] (prob) -- (book);
% \end{scope}
\end{tikzpicture}
\caption{Dependency tree: prerequisites and how they feed into the main text.}
\label{fig:dependency-tree}
\end{figure}
\ifSubfilesClassLoaded{
\printbibliography[title={References}]
}
\end{document}
% preface.tex
\documentclass[main.tex]{subfiles}
\ifSubfilesClassLoaded{
\addbibresource{main.bib}
}
\begin{document}
\chapter*{Preface}
\addcontentsline{toc}{chapter}{Preface}
Non-commutative probability theory is a branch of generalized probability theory that studies the probability of events in non-commutative algebras (e.g. the algebra of observables in quantum mechanics). In the 20th century, non-commutative probability theory has been applied to the study of quantum mechanics as the classical probability theory is not enough to describe quantum mechanics~\cite{kummer1998elements}.
Recently, the concentration of measure phenomenon has been applied to the study of non-commutative probability theory. Basically, the non-trivial observation, citing from Gromov's work~\cite{MGomolovs}, states that an arbitrary 1-Lipschitz function $f:S^n\to \mathbb{R}$ concentrates near a single value $a_0\in \mathbb{R}$ as strongly as the distance function does. That is,
$$
\mu\{x\in S^n: |f(x)-a_0|\geq\epsilon\} < \kappa_n(\epsilon)\leq 2\exp\left(-\frac{(n-1)\epsilon^2}{2}\right)
$$
is applied to computing the probability that, given a bipartite system $A\otimes B$, assume $\dim(B)\geq \dim(A)\geq 3$, as the dimension of the smaller system $A$ increases, with very high probability, a random pure state $\sigma=|\psi\rangle\langle\psi|$ selected from $A\otimes B$ is almost as good as the maximally entangled state.
Mathematically, that is:
Let $\psi\in \mathcal{P}(A\otimes B)$ be a random pure state on $A\otimes B$.
If we define $\beta=\frac{1}{\ln(2)}\frac{d_A}{d_B}$, then we have
$$
\operatorname{Pr}[H(\psi_A) < \log_2(d_A)-\alpha-\beta] \leq \exp\left(-\frac{1}{8\pi^2\ln(2)}\frac{(d_Ad_B-1)\alpha^2}{(\log_2(d_A))^2}\right)
$$
where $d_B\geq d_A\geq 3$~\cite{Hayden_2006}.
In this report, we will show the process of my exploration of the concentration of measure phenomenon in the context of non-commutative probability theory. We assume the reader is an undergraduate student in mathematics and is familiar with the basic concepts of probability theory, measure theory, linear algebra, and some basic skills of mathematical analysis. To make the report more self-contained, we will add detailed annotated proofs that I understand and references for the original sources.
\section*{How to use the dependency graph}
Since our topic integrates almost everything I've learned during undergraduate study, I will try to make some dependency graph for reader and for me to keep track of what are the necessary knowledge to understand part of the report.
One can imagine the project as a big tree, where the root is in undergrad math and branches out to the topics of the report, including many advanced topics and motivation to study them.
\bigskip
% --- Dependency tree graph (TikZ) ---
\begin{figure}[ht]
\centering
\begin{tikzpicture}[
node distance=10mm and 18mm,
box/.style={draw, rectangle, fill=white, align=center, inner sep=4pt},
arrow/.style={-Latex}
]
% \node[box] (lin) {Linear Algebra\\(bases, maps, eigenvalues)};
% \node[box, right=of lin] (real) {Real Analysis\\(limits, continuity, measure-lite)};
% \node[box, below=of lin] (prob) {Probability\\(expectation, variance, concentration)};
% \node[box, below=of real] (top) {Topology/Geometry\\(metrics, compactness)};
% \node[box, below=12mm of prob] (func) {Functional Analysis\\($L^p$, Hilbert spaces, operators)};
% \node[box, below=12mm of top] (quant) {Quantum Formalism\\(states, observables, partial trace)};
% \node[box, below=14mm of func, xshift=25mm] (book) {This Book\\(Chapters 1--n)};
% % draw arrows behind nodes
% \begin{scope}[on background layer]
% \draw[arrow] (lin) -- (func);
% \draw[arrow] (real) -- (func);
% \draw[arrow] (prob) -- (func);
% \draw[arrow] (func) -- (quant);
% \draw[arrow] (lin) -- (quant);
% \draw[arrow] (top) -- (quant);
% \draw[arrow] (func) -- (book);
% \draw[arrow] (quant) -- (book);
% \draw[arrow] (prob) -- (book);
% \end{scope}
\end{tikzpicture}
\caption{Dependency tree: prerequisites and how they feed into the main text.}
\label{fig:dependency-tree}
\end{figure}
\ifSubfilesClassLoaded{
\printbibliography[title={References}]
}
\end{document}

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