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Quantum Machine Learning

Software for Massively Parallel Quantum Computing

arXiv
Authors: Thien Nguyen, Daanish Arya, Marcus Doherty, Nils Herrmann, Johannes Kuhlmann, Florian Preis, Pat Scott, Simon Yin

Year

2022

Paper ID

6544

Status

Preprint

Abstract Read

~2 min

Abstract Words

101

Citations

N/A

Abstract

Quantum computing has the potential to offer substantial computational advantages over conventional computing. Recent advances in quantum computing hardware and algorithms have enabled a class of classically parallel quantum workloads, whereby individual quantum circuits can execute independently on many quantum processing units. Here, we present the full-stack software framework developed at Quantum Brilliance to enable multi-modal parallelism for hybrid quantum workloads. Our software provides the capability to distribute quantum workloads across multiple quantum accelerators hosted by nodes of a locally-networked cluster, via the industry-standard MPI (Message Passing Interface) protocol, or to distribute workloads across a large number of cloud-hosted quantum accelerators.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2022 reference point for readers tracking recent quantum research.
  • Quantum computing has the potential to offer substantial computational advantages over conventional computing.

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