Efficient embedding to solve the quantum linear systems problem in near-term quantum processors

Sean T. Crowe; Ramiro Rodriguez; Daniel Gunlycke; Fernando Escobar; Joanna N. Ptasinski

doi:10.1117/12.2632069

4 October 2022 Efficient embedding to solve the quantum linear systems problem in near-term quantum processors

Sean T. Crowe, Ramiro Rodriguez, Daniel Gunlycke, Fernando Escobar, Joanna N. Ptasinski

Proceedings Volume 12238, Quantum Communications and Quantum Imaging XX; 122380A (2022) https://doi.org/10.1117/12.2632069
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

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

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available