用混合精度提升量子模拟效率，采样环节可降为半精度无损失
Neural Quantum States in Mixed Precision
- 提出混合精度在蒙特卡洛采样中的误差理论边界
- 实测表明量子态采样可用半精度保持精度，性能大幅提升
- 适合关注量子模拟加速与能效的计算物理研究者
科学计算长期依赖双精度（64位浮点）以保证真实现象模拟的准确性。然而，随着图形处理器（GPU）等硬件加速器的普及，低精度格式因其更高的性能、更小的内存占用和更好的能效而变得具有吸引力。本文研究了混合精度算术在基于神经网络的变分蒙特卡洛（VMC）方法中的作用，该方法广泛用于求解原本计算上不可行的量子多体系统。我们首先推导出降低精度对马尔可夫链蒙特卡洛（Metropolis-Hastings MCMC）引入误差的一般解析界限，并在 VMC 的使用场景中实证验证这些界限。结果表明，算法中相当大一部分，特别是量子态采样环节，可在半精度下执行而不会造成精度损失。本工作还为依赖 MCMC 采样的机器学习方法提供了评估混合精度适用性的理论框架。在 VMC 情境下，进一步展示了混合精度策略的实际有效性，使量子多体系统的模拟更具可扩展性和能源效率。
原文摘要 · Abstract (English)
Scientific computing has long relied on double precision (64-bit floating point) arithmetic to guarantee accuracy in simulations of real-world phenomena. However, the growing availability of hardware accelerators such as Graphics Processing Units (GPUs) has made low-precision formats attractive due to their superior performance, reduced memory footprint, and improved energy efficiency. In this work, we investigate the role of mixed-precision arithmetic in neural-network based Variational Monte Carlo (VMC), a widely used method for solving computationally otherwise intractable quantum many-body systems. We first derive general analytical bounds on the error introduced by reduced precision on Metropolis-Hastings MCMC, and then empirically validate these bounds on the use-case of VMC. We demonstrate that significant portions of the algorithm, in particular, sampling the quantum state, can be executed in half precision without loss of accuracy. More broadly, this work provides a theoretical framework to assess the applicability of mixed-precision arithmetic in machine-learning approaches that rely on MCMC sampling. In the context of VMC, we additionally demonstrate the practical effectiveness of mixed-precision strategies, enabling more scalable and energy-efficient simulations of quantum many-body systems.
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