Modernizing quantum annealing II: genetic algorithms with the inference primitive formalism

Chancellor, Nicholas

doi:10.1007/s11047-022-09905-2

Modernizing quantum annealing II: genetic algorithms with the inference primitive formalism

Chancellor, Nicholas

Authors

Dr Nicholas Chancellor nicholas.chancellor@durham.ac.uk
Teaching Fellow QO

Abstract

Quantum annealing, a method of computing where optimization and machine learning problems are mapped to physically implemented energy landscapes subject to quantum fluctuations, allows for these fluctuations to be used to assist in finding the solution to some of the world’s most challenging computational problems. Recently, this field has attracted much interest because of the construction of large-scale flux-qubit based quantum annealing devices. These devices have since implemented a technique known as reverse annealing which allows the solution space to be searched locally, and algorithms based on these techniques have been tested. In this paper, I develop a formalism for algorithmic design in quantum annealers, which I call the ‘inference primitive’ formalism. This formalism naturally lends itself to expressing algorithms which are structurally similar to genetic algorithms, but where the annealing processor performs a combined crossover/mutation step. I demonstrate how these methods can be used to understand the algorithms which have already been implemented and the compatibility of such controls with a wide variety of other current efforts to improve the performance of quantum annealers.

Citation

Chancellor, N. (2023). Modernizing quantum annealing II: genetic algorithms with the inference primitive formalism. Natural Computing, 22, 737–752. https://doi.org/10.1007/s11047-022-09905-2

Journal Article Type	Article
Acceptance Date	Jul 3, 2022
Online Publication Date	Jul 23, 2022
Publication Date	2023-12
Deposit Date	Jul 25, 2022
Publicly Available Date	Jul 25, 2022
Journal	Natural Computing
Print ISSN	1567-7818
Electronic ISSN	1572-9796
Publisher	Springer
Peer Reviewed	Peer Reviewed
Volume	22
Pages	737–752
DOI	https://doi.org/10.1007/s11047-022-09905-2
Public URL	https://durham-repository.worktribe.com/output/1196921

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