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Quantum Machine Learning

Introducing the Quantum Research Kernels: Lessons from Classical Parallel Computing

arXiv

Authors: A. Y. Matsuura, Timothy G. Mattson

Year

2022

Paper ID

57769

Status

Preprint

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~2 min

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Abstract

Quantum computing represents a paradigm shift for computation requiring an entirely new computer architecture. However, there is much that can be learned from traditional classical computer engineering. In this paper, we describe the Parallel Research Kernels (PRK), a tool that was very useful for designing classical parallel computing systems. The PRK are simple kernels written to expose bottlenecks that limit classical parallel computing performance. We hypothesize that an analogous tool for quantum computing, Quantum Research Kernels (QRK), may similarly aid the co-design of software and hardware for quantum computing systems, and we give a few examples of representative QRKs.

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 represents a paradigm shift for computation requiring an entirely new computer architecture.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2211.00844 [2] arXiv https://arxiv.org/abs/2211.00844 [3] Publisher https://arxiv.org/abs/2211.00844

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