AI & ML
Parallel Repetition for Entangled Games via Conditional Relative Entropy
arXiv math.IT0 views
arXiv:2610.21410v1 Announce Type: cross Abstract: We give a proof of the cubic parallel-repetition bound for finite two-player entangled games using quantum conditional relative entropy. For a game with entangled value $1-\varepsilon<1$ and answer alphabets $\mathcal A,\mathcal B$, its $n$-fold repeated value is at most \[ \exp[-\Omega(\varepsilon^3n/L)],\qquad L:=\max\{1,\log(|\mathcal A||\mathcal B|)\}, \] for every $n\ge1$. This recovers Song's cubic rate. We also prove a threshold bound: the probability of winning a fraction at least $1-\varepsilon+\delta$ is at most $\exp[-\Omega(\delta^5n/(\varepsilon^2L))]$ for $0<\delta\le\varepsilon$. Our information charge is the decrease in relative entropy when a question reference is discarded. Our comparison states preserve the original conditional question law. A chain rule bounds the coordinate charges using the cost of conditioning on checked wins and retaining checked answers. Our main lemma converts small charges into nearby pure-state descriptions and local corrections, each depending only on one player's question. Together with Song's sampler, these enable approximate local preparation of the conditioned workspace.
Source:https://arxiv.org/abs/2610.21410PreviousThe Rate of the Delsarte Bound for Binary Codes and SpheresNextDecoding Failure Biases IBLT Count Measurements: A Gaussian-Selection Law at the Peeling Threshold
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