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DTSTAMP:20211207T054807Z
LOCATION:220-221
DTSTART;TZID=America/Chicago:20211117T160000
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UID:submissions.supercomputing.org_SC21_sess156_pap563@linklings.com
SUMMARY:Overcoming Barriers to Scalability in Variational Quantum Monte Ca
 rlo
DESCRIPTION:Paper\n\nOvercoming Barriers to Scalability in Variational Qua
 ntum Monte Carlo\n\nZhao, Chen, De, Stokes, Veerapaneni\n\nThe variational
  quantum Monte Carlo (VQMC) method received significant attention in the r
 ecent 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 variat
 ional quantum algorithms. VQMC overcomes the curse of dimensionality by pe
 rforming alternating steps of Monte Carlo sampling from a parametrized qua
 ntum state followed by gradient-based optimization.\n\nWhile VQMC has been
  applied to solve high-dimensional problems, it is known to be difficult t
 o parallelize, primarily owing to the Markov Chain Monte Carlo (MCMC) samp
 ling step. In this work, we explore the scalability of VQMC when autoregre
 ssive models, with exact sampling, are used in place of MCMC. This approac
 h can exploit distributed-memory, shared-memory and/or GPU parallelism in 
 the sampling task without any bottlenecks. In particular, we demonstrate t
 he GPU-scalability of VQMC for solving up to ten-thousand dimensional comb
 inatorial optimization problems.\n\nTag: Applications, Heterogeneous Syste
 ms, Scientific Computing\n\nRegistration Category: Tech Program Reg Pass
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