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Quantum Optimization
Quantum Machine Learning
Toward a Unified Hybrid HPCQC Toolchain
arXiv
Authors: Philipp Seitz, Amr Elsharkawy, Xiao-Ting Michelle To, Martin Schulz
Year
2023
Paper ID
55196
Status
Preprint
Abstract Read
~2 min
Abstract Words
80
Citations
N/A
Abstract
In the expanding field of Quantum Computing (QC), efficient and seamless integration of QC and high performance computing (HPC) elements (e.g., quantum hardware, classical hardware, and software infrastructure on both sides) plays a crucial role. This paper addresses the development of a unified toolchain designed for hybrid quantum-classical systems. Our work proposes a design for a unified hybrid high performance computing - quantum computing (HPCQC) toolchain that tackles pressing issues such as scalability, cross-technology execution, and ahead-of-time (AOT) optimization.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2023 reference point for readers tracking recent quantum research.
- In the expanding field of Quantum Computing (QC), efficient and seamless integration of QC and high performance computing (HPC) elements (e.g., quantum hardware, classical...
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