The variational quantum Monte Carlo (VQMC) method received significant attention in the recent past because of its ability to overcome the curse of dimensionality inherent in many-body quantum systems. Close parallels exist between VQMC and the emerging hybrid quantum-classical computational paradigm of variational quantum algorithms. VQMC overcomes the curse of dimensionality by performing alternating steps of Monte Carlo sampling from a parametrized quantum state followed by gradient-based optimization. While VQMC has been applied to solve high-dimensional problems, it is known to be difficult to parallelize, primarily owing to the Markov Chain Monte Carlo (MCMC) sampling step. In this work, we explore the scalability of VQMC when autoregressive models, with exact sampling, are used in place of MCMC. This approach can exploit distributed-memory, shared-memory and/or GPU parallelism in the sampling task without any bottlenecks. In particular, we demonstrate the GPU-scalability of VQMC for solving up to ten-thousand dimensional combinatorial optimization problems.
翻译:最近,变量量蒙特卡洛(VQMC)方法由于能够克服多体量子系统中内在的维度诅咒而在最近受到极大关注。VQMC与变化量子算法的新兴混合量子-古典计算范式存在密切的平行关系。VQMC通过从一个以斜度为基础的优化后,从一个超模化量子状态轮流进行蒙特卡洛取样,克服了维度的诅咒。虽然VQMC被用于解决高维度问题,但众所周知,主要由于Markov链蒙特卡洛(MMC)取样步骤,它难以平行。在这项工作中,我们探索VQMC在使用以精确取样的自动递减模型时,VQMC的可伸缩性。这种方法可以在取样任务中利用分布式-模量、共享模擬和/或GPU的平行性,而没有任何瓶颈。我们特别证明了VQMC在解决十维分维调调调整问题时的GPU-缩放。