标题： 用于量子模拟的随机伊辛机的采样优势预测 显示英文标题

标题： Predicting sampling advantage of stochastic Ising Machines for Quantum Simulations

摘要： 随机伊辛机（sIMs）是非常有前景的优化和计算问题采样加速器，这些问题可以表述为伊辛模型。 在这里，我们研究了sIM在使用神经网络量子态（NQS）模拟量子磁体时的计算优势，在这种情况下，量子多体波函数被映射到一个伊辛模型上。 我们通过比较软件仿真出的sIM的采样与标准的针对NQS的Metropolis-Hastings采样，来研究sIM对NQS的采样性能。 我们通过达到变分能量随机估计的等精度所需的步数来量化采样效率，并表明这完全由采样的自相关时间决定。 这使得在硬件上直接部署之前能够预测采样优势。 对于所研究的量子海森堡模型和sIM的运行时间实验结果，我们预测可能的速度提升在100到10000之间，这表明在更大规模上研究复杂量子系统具有巨大的机会。 显示英文摘要

摘要： Stochastic Ising machines, sIMs, are highly promising accelerators for optimization and sampling of computational problems that can be formulated as an Ising model. Here we investigate the computational advantage of sIM for simulations of quantum magnets with neural-network quantum states (NQS), in which the quantum many-body wave function is mapped onto an Ising model. We study the sampling performance of sIM for NQS by comparing sampling on a software-emulated sIM with standard Metropolis-Hastings sampling for NQS. We quantify the sampling efficiency by the number of steps required to reach iso-accurate stochastic estimation of the variational energy and show that this is entirely determined by the autocorrelation time of the sampling. This enables predications of sampling advantage without direct deployment on hardware. For the quantum Heisenberg models studied and experimental results on the runtime of sIMs, we project a possible speed-up of 100 to 10000, suggesting great opportunities for studying complex quantum systems at larger scales.