Title: Artificial-Intelligence-Driven Shot Reduction in Quantum Measurement Show Chinese title

Title: 基于人工智能的量子测量中的快照减少

Abstract: 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. Show Chinese abstract

Abstract: 变分量子本征求解器（VQE）通过结合量子电路和经典计算机，为近似分子基态能量提供了强大的解决方案。 然而，在量子硬件上估计概率结果需要重复测量（次数），随着准确性的提高，成本显著增加。 因此，优化次数分配对于提高VQE的效率至关重要。 当前策略严重依赖于需要大量专家知识的手工启发式方法。 本文提出了一种基于强化学习（RL）的方法，该方法自动学习次数分配策略，在实现VQE中能量期望最小值收敛的同时，最小化总测量次数。 RL代理根据优化的进展，在VQE优化迭代中分配测量次数。 这种方法减少了VQE对静态启发式方法和人工经验的依赖。 当将启用RL的VQE应用于小分子时，学习到了一种次数减少策略。 该策略在不同系统中表现出可迁移性，并与其他波函数ansatz兼容。 除了这些具体发现外，这项工作突显了RL在自动发现高效且可扩展的量子优化策略方面的潜力。