用生成模型模拟测量扰动下的量子临界现象
Generative modeling assisted simulation of measurement-altered quantum criticality
- 构建物理保真条件扩散模型，从测量结果生成量子态
- 成功生成临界链的局部约化密度矩阵，实现测量诱导相变模拟
- 适合研究量子测量与相变关系的理论物理与量子信息研究者
在量子多体系统中，测量可引发定性新特征，但其模拟受限于测量结果采样带来的指数级复杂度。本文提出利用机器学习辅助模拟测量诱导的量子现象。重点关注测量扰动的量子临界性协议，基于随机测量结果生成临界链的局部约化密度矩阵。该生成通过一个物理保真的条件扩散生成模型实现，该模型学习量子态集合在观测索引下的概率分布，并在给定观测条件下进行采样。
原文摘要 · Abstract (English)
In quantum many-body systems, measurements can induce qualitative new features, but their simulation is hindered by the exponential complexity involved in sampling the measurement results. We propose to use machine learning to assist the simulation of measurement-induced quantum phenomena. In particular, we focus on the measurement-altered quantum criticality protocol and generate local reduced density matrices of the critical chain given random measurement results. Such generation is enabled by a physics-preserving conditional diffusion generative model, which learns an observation-indexed probability distribution of an ensemble of quantum states, and then samples from that distribution given an observation.
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