用强化学习优化量子硬件上的薛定谔-叶-基塔耶夫模型热态制备，显著减少门数。
Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardware
- 结合卷积神经网络的强化学习迭代优化量子线路参数。
- 对N≥12系统，CNOT门数降低两个数量级，优于一阶刘维尔方法。
- 适用于噪声与无噪声环境，适合量子引力与关联函数研究。
薛定谔-叶-基塔耶夫（SYK）模型以其强量子关联和混沌行为，成为量子引力研究的重要平台。然而，在近中期量子处理器上为大系统（N>12，N为马约拉纳费米子数）变分制备热态面临巨大挑战，源于参数化量子线路复杂度随规模急剧增长。本文通过将强化学习（RL）与卷积神经网络结合，采用迭代优化策略，以熵和SYK哈密顿量期望值构成复合奖励信号，指导量子线路及参数优化。该方法在N≥12系统中使CNOT门数减少两个数量级，显著优于传统的一阶刘维尔分解法。我们在无噪声与含噪声量子硬件环境中均验证了该框架的有效性，热态制备保持高精度。本工作推进了一种可扩展的基于强化学习的框架，可用于近中期量子硬件上的量子引力研究及量子多体系统的非时序有序关联函数计算。代码已开源：https://github.com/Aqasch/solving_SYK_model_with_RL。
原文摘要 · Abstract (English)
The Sachdev-Ye-Kitaev (SYK) model, known for its strong quantum correlations and chaotic behavior, serves as a key platform for quantum gravity studies. However, variationally preparing thermal states on near-term quantum processors for large systems ($N>12$, where $N$ is the number of Majorana fermions) presents a significant challenge due to the rapid growth in the complexity of parameterized quantum circuits. This paper addresses this challenge by integrating reinforcement learning (RL) with convolutional neural networks, employing an iterative approach to optimize the quantum circuit and its parameters. The refinement process is guided by a composite reward signal derived from entropy and the expectation values of the SYK Hamiltonian. This approach reduces the number of CNOT gates by two orders of magnitude for systems $N\geq12$ compared to traditional methods like first-order Trotterization. We demonstrate the effectiveness of the RL framework in both noiseless and noisy quantum hardware environments, maintaining high accuracy in thermal state preparation. This work advances a scalable, RL-based framework with applications for quantum gravity studies and out-of-time-ordered thermal correlators computation in quantum many-body systems on near-term quantum hardware. The code is available at https://github.com/Aqasch/solving_SYK_model_with_RL.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。