用强化学习自动设计量子电路，显著减少门数和深度。
Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning
- 用双深度Q网络将电路设计视为多目标优化问题。
- 在最大割问题上减少37%门数、43%深度，氢分子达全配置积分精度。
- 适合需要高效量子算法设计的研究者，尤其关注硬件约束的场景。
高效寻找基态是推进组合优化与量子化学的关键。尽管变分虚时间演化（VITE）为变分量子本征值求解器（VQE）和量子近似优化算法（QAOA）提供了替代方案，但其在噪声中等规模量子（NISQ）设备上的实现受限于手动设计的试探态门数和深度。本文提出一种基于双深度Q网络（DDQN）的自动化VITE电路设计框架。该方法将电路构建视为多目标优化问题，同时最小化能量期望值并优化电路复杂度。通过引入自适应阈值，显著降低硬件开销。在最大割问题中，智能体自主发现的电路平均比标准硬件高效试探态少约37%的门数和43%的深度；对于氢分子（H₂），DDQN也达到了全配置积分（Full-CI）极限，且保持更浅的电路结构。结果表明，深度强化学习有助于发现非直观的最优电路结构，为高效、硬件感知的量子算法设计提供新路径。
原文摘要 · Abstract (English)
Efficient ground state search is fundamental to advancing combinatorial optimization problems and quantum chemistry. While the Variational Imaginary Time Evolution (VITE) method offers a useful alternative to Variational Quantum Eigensolver (VQE), and Quantum Approximate Optimization Algorithm (QAOA), its implementation on Noisy Intermediate-Scale Quantum (NISQ) devices is severely limited by the gate counts and depth of manually designed ansatz. Here, we present an automated framework for VITE circuit design using Double Deep-Q Networks (DDQN). Our approach treats circuit construction as a multi-objective optimization problem, simultaneously minimizing energy expectation values and optimizing circuit complexity. By introducing adoptive thresholds, we demonstrate significant hardware overhead reductions. In Max-Cut problems, our agent autonomously discovered circuits with approximately 37\% fewer gates and 43\% less depth than standard hardware-efficient ansatz on average. For molecular hydrogen ($H_2$), the DDQN also achieved the Full-CI limit, with maintaining a significantly shallower circuit. These results suggest that deep reinforcement learning can be helpful to find non-intuitive, optimal circuit structures, providing a pathway toward efficient, hardware-aware quantum algorithm design.
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