Feedback-based quantum algorithm inspired by counterdiabatic driving

In recent quantum algorithmic developments, a feedback-based approach has shown promise for preparing quantum many-body system ground states and solving combinatorial optimization problems. This method utilizes quantum Lyapunov control to iteratively construct quantum circuits. Here, we propose a su...

Full description

Saved in:
Bibliographic Details
Published in:Physical review research Vol. 6; no. 4; p. 043068
Main Authors: Malla, Rajesh K., Sukeno, Hiroki, Yu, Hongye, Wei, Tzu-Chieh, Weichselbaum, Andreas, Konik, Robert M.
Format: Journal Article
Language:English
Published: United States American Physical Society (APS) 01.10.2024
American Physical Society
Subjects:
CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY
quantum algorithms and computation
quantum circuits
quantum computation
quantum control
quantum feedback
quantum protocols
quantum simulation
ISSN:2643-1564, 2643-1564
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In recent quantum algorithmic developments, a feedback-based approach has shown promise for preparing quantum many-body system ground states and solving combinatorial optimization problems. This method utilizes quantum Lyapunov control to iteratively construct quantum circuits. Here, we propose a substantial enhancement by implementing a protocol that uses ideas from quantum Lyapunov control and the counterdiabatic driving protocol, a key concept from quantum adiabaticity. Our approach introduces an additional control field inspired by counterdiabatic driving. We apply our algorithm to prepare ground states in one-dimensional quantum Ising spin chains. Comprehensive simulations demonstrate a remarkable acceleration in population transfer to low-energy states within a significantly reduced time frame compared to conventional feedback-based quantum algorithms. This acceleration translates to a reduced quantum circuit depth, a critical metric for potential quantum computer implementation. We validate our algorithm on the IBM cloud computer, highlighting its efficacy in expediting quantum computations for many-body systems and combinatorial optimization problems.
Bibliography:SC0012704; AC05-00OR22725
USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
BNL-226341-2024-JAAM
ISSN:2643-1564
2643-1564
DOI:10.1103/PhysRevResearch.6.043068