Presentation + Paper
4 October 2022 Efficient embedding to solve the quantum linear systems problem in near-term quantum processors
Author Affiliations +
Abstract
Recent advances in quantum machine learning and quantum state embedding are integrated, providing a resource efficient framework for solutions of linear systems on Noisy Intermediate Scale Quantum (NISQ) machines. A divide and conquer algorithm is used to embed the indexing vector after which the Coherent Variational Quantum Linear Solver (CVQLS) algorithm is used to invert the problem matrix. This integrated procedure has an improved complexity scaling in the quantum resources needed to execute and produces solutions which agree with what is found classically.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sean T. Crowe, Ramiro Rodriguez, Daniel Gunlycke, Fernando Escobar, and Joanna N. Ptasinski "Efficient embedding to solve the quantum linear systems problem in near-term quantum processors", Proc. SPIE 12238, Quantum Communications and Quantum Imaging XX, 122380A (4 October 2022); https://doi.org/10.1117/12.2632069
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KEYWORDS
Quantum communications

Computing systems

Quantum computing

Quantum efficiency

Matrices

Quantum information

Machine learning

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