Overcoming Barriers to Scalability in Variational Quantum Monte Carlo
Overcoming Barriers to Scalability in Variational Quantum Monte Carlo
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DOI：
10.1145/3458817.3476219
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Tianchen Zhao-;Saibal De;Brian Chen;J. Stokes;S. Veerapaneni
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作者：
Tianchen Zhao-;Saibal De;Brian Chen;J. Stokes;S. Veerapaneni
变分量子蒙特卡罗（VQMC）方法由于能够克服多体量子系统中固有的维数灾难而受到广泛关注。VQMC和新兴的混合量子-经典计算范式变分量子算法之间存在密切的相似之处。VQMC通过执行从参数化量子态进行蒙特卡罗采样的交替步骤以及随后的基于梯度的优化来克服维数灾难。虽然VQMC已被应用于解决高维问题，它是已知的难以并行化，主要是由于马尔可夫链蒙特卡罗（MCMC）采样步骤。在这项工作中，我们探讨了自回归模型，精确采样时，在MCMC的地方使用的VQMC的可扩展性。这种方法可以在采样任务中利用分布式存储器、共享存储器和/或GPU并行性而没有任何瓶颈。特别是，我们证明了GPU的可扩展性的VQMC解决高达一万维的组合优化问题。
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 GPU-scalability of VQMC for solving up to ten-thousand dimensional combinatorial optimization problems.