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

Chancellor, Nicholas

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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.


Chancellor, N. (2023). Modernizing quantum annealing II: genetic algorithms with the inference primitive formalism. Natural Computing, 22, 737–752.

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
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Published Journal Article (874 Kb)

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