人工智能驱动的量子测量中的发射次数缩减
Artificial-Intelligence-Driven Shot Reduction in Quantum Measurement
AI总结：
本文提出基于强化学习的方法自动学习VQE中的发射次数分配策略，以最小化总测量次数并实现能量收敛，实验证明该策略具有跨系统可迁移性和波函数拟设兼容性。
摘要：
变分量子本征求解器（VQE）通过结合量子电路与经典计算机，为近似分子基态能量提供了一种强大的解决方案。然而，在量子硬件上估计概率性结果需要重复测量（发射次数），随着精度要求的提高，这会带来显著的成本。因此，优化发射次数分配对于提高VQE的效率至关重要。当前的策略严重依赖手工设计的启发式方法，需要大量的专家知识。本文提出了一种基于强化学习（RL）的方法，该方法能够自动学习发射次数分配策略，以最小化总测量发射次数，同时在VQE中实现能量期望最小值的收敛。RL智能体根据优化过程的进展，在VQE优化迭代中分配测量发射次数。该方法减少了VQE对静态启发式方法和人类专业知识的依赖。当将启用RL的VQE应用于一个小分子时，学习到了一个发射次数缩减策略。该策略展示了跨系统的可迁移性以及与其他波函数拟设的兼容性。除了这些具体发现之外，本工作还突显了强化学习在自动发现高效且可扩展的量子优化策略方面的潜力。
英文摘要：
Variational Quantum Eigensolver (VQE) provides a powerful solution for approximating molecular ground state energies by combining quantum circuits and classical computers. However, estimating probabilistic outcomes on quantum hardware requires repeated measurements (shots), incurring significant costs as accuracy increases. Optimizing shot allocation is thus critical for improving the efficiency of VQE. Current strategies rely heavily on hand-crafted heuristics requiring extensive expert knowledge. This paper proposes a reinforcement learning (RL) based approach that automatically learns shot assignment policies to minimize total measurement shots while achieving convergence to the minimum of the energy expectation in VQE. The RL agent assigns measurement shots across VQE optimization iterations based on the progress of the optimization. This approach reduces VQE's dependence on static heuristics and human expertise. When the RL-enabled VQE is applied to a small molecule, a shot reduction policy is learned. The policy demonstrates transferability across systems and compatibility with other wavefunction ansatzes. In addition to these specific findings, this work highlights the potential of RL for automatically discovering efficient and scalable quantum optimization strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
