Gibbs Sampling in the Shattered Phase by Decoded Quantum Interferometry

arXiv:2609.40345v1 Announce Type: new Abstract: We apply Decoded Quantum Interferometry (DQI) to sample from the Gibbs measures of classical Ising spin Hamiltonians. We show that this Gibbs sampling problem reduces to a quantum decoding problem, and the temperature achievable by DQI is determined by the performance of decoding algorithms. We then focus on the task of Gibbs sampling for classical Ising $k$-spin glasses (or Max-$k$-XORSAT) on random Erd\H{o}s-R\'enyi hypergraphs with average degree $D\ge k$. In a temperature range beginning asymptotically at the predicted dynamical phase transition, $\beta_{\rm dyn}(k,D) = \sqrt{(2\ln k)/D}\times [1+o_{k\to\infty}(1)]$, we show that shattering and disorder chaos form a topological barrier that obstructs many algorithms, including Glauber dynamics and any algorithm whose output distribution is "stable" under perturbations of the input. In contrast, we prove that this barrier can be broken both by a classical algorithm based on Prange's method, and by DQI equipped with a quantum decoder. For example, when $D=\alpha k$ with fixed $\alpha>1$, both Prange's algorithm and DQI can sample at any inverse temperature $\beta < \tanh^{-1}(1/\alpha)$ for sufficiently large $k$, well beyond the dynamical threshold $\beta_{\rm dyn} \sim \sqrt{2\ln k / (\alpha k)}$. Therefore, our results show that DQI can overcome topological barriers that obstruct stable algorithms.

Abstract:We apply Decoded Quantum Interferometry (DQI) to sample from the Gibbs measures of classical Ising spin Hamiltonians. We show that this Gibbs sampling problem reduces to a quantum decoding problem, and the temperature achievable by DQI is determined by the performance of decoding algorithms. We then focus on the task of Gibbs sampling for classical Ising $k$-spin glasses (or Max-$k$-XORSAT) on random Erdős-Rényi hypergraphs with average degree $D\ge k$. In a temperature range beginning asymptotically at the predicted dynamical phase transition, $\beta_{\rm dyn}(k,D) = \sqrt{(2\ln k)/D}\times [1+o_{k\to\infty}(1)]$, we show that shattering and disorder chaos form a topological barrier that obstructs many algorithms, including Glauber dynamics and any algorithm whose output distribution is "stable" under perturbations of the input. In contrast, we prove that this barrier can be broken both by a classical algorithm based on Prange's method, and by DQI equipped with a quantum decoder. For example, when $D=\alpha k$ with fixed $\alpha>1$, both Prange's algorithm and DQI can sample at any inverse temperature $\beta < \tanh^{-1}(1/\alpha)$ for sufficiently large $k$, well beyond the dynamical threshold $\beta_{\rm dyn} \sim \sqrt{2\ln k / (\alpha k)}$. Therefore, our results show that DQI can overcome topological barriers that obstruct stable algorithms.

Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS); Probability (math.PR)

arXiv:2609.40345 [quant-ph]

(or arXiv:2609.40345v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.40345 arXiv-issued DOI via DataCite (pending registration)

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Gibbs Sampling in the Shattered Phase by Decoded Quantum Interferometry

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