论文标题

带有概率计算机的加速量子蒙特卡洛

Accelerated Quantum Monte Carlo with Probabilistic Computers

论文作者

Chowdhury, Shuvro, Camsari, Kerem Y., Datta, Supriyo

论文摘要

量子蒙特卡洛(QMC)技术被广泛用于各种科学问题,并且已经致力于开发可以加速QMC在标准处理器(CPU)上加速QMC的优化算法。随着各种特殊目的设备和特定领域的特定硬件的出现,与现有技术相比,建立这些技术提供的改进的明确基准变得越来越重要。在本文中,我们使用特殊设计的数字处理器演示了标准QMC算法的2至3个数量级加速度,并通过将其映射到无钟模拟处理器来绘制到另外2至3个数量级。我们的演示为横向场Ising模型(TFIM)提供了5至6个数量级加速度的路线图,并且也可以扩展到其他QMC模型。无时钟的模拟硬件可以看作是量子退火器的经典对应物,并在后者的$ <10 $范围内提供性能。无时钟模拟硬件量表的收敛时间,量子位为$ \ sim n $,改善了CPU实现的$ \ sim n^2 $缩放时间,但似乎比通过D-Wave报告的量子退火器报告的要差。

Quantum Monte Carlo (QMC) techniques are widely used in a variety of scientific problems and much work has been dedicated to developing optimized algorithms that can accelerate QMC on standard processors (CPU). With the advent of various special purpose devices and domain specific hardware, it has become increasingly important to establish clear benchmarks of what improvements these technologies offer compared to existing technologies. In this paper, we demonstrate 2 to 3 orders of magnitude acceleration of a standard QMC algorithm using a specially designed digital processor, and a further 2 to 3 orders of magnitude by mapping it to a clockless analog processor. Our demonstration provides a roadmap for 5 to 6 orders of magnitude acceleration for a transverse field Ising model (TFIM) and could possibly be extended to other QMC models as well. The clockless analog hardware can be viewed as the classical counterpart of the quantum annealer and provides performance within a factor of $<10$ of the latter. The convergence time for the clockless analog hardware scales with the number of qubits as $\sim N$, improving the $\sim N^2$ scaling for CPU implementations, but appears worse than that reported for quantum annealers by D-Wave.

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