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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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