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Experimental demonstration of improved quantum optimization with linear Ising penalties [dataset] Open Access

The standard approach to encoding constraints in quantum optimization is the quadratic penalty method. Quadratic penalties introduce additional couplings and energy scales, which can be detrimental to the performance of a quantum optimizer. In quantum annealing experiments performed on a D-Wave Advantage, we explore an alternative penalty method that only involves linear Ising terms and apply it to a customer data science problem. Our findings support our hypothesis that the linear Ising penalty method should improve the performance of quantum optimization compared to using the quadratic penalty method due to its more efficient use of physical resources. Although the linear Ising penalty method is not guaranteed to exactly implement the desired constraint in all cases, it is able to do so for the majority of problem instances we consider. For problems with many constraints, where making all penalties linear is unlikely to be feasible, we investigate strategies for combining linear Ising penalties with quadratic penalties to satisfy constraints for which the linear method is not well-suited. We find that this strategy is most effective when the penalties that contribute most to limiting the dynamic range are removed.

Descriptions

Resource type

Dataset

Contributors

Editor: Mirkarimi, Puya ¹
Creator: Mirkarimi, Puya ¹
Data collector: Mirkarimi, Puya ¹
Contact person: Mirkarimi, Puya ¹
Data curator: Mirkarimi, Puya ¹
Creator: Hoyle, David C. ²
Creator: Williams, Ross ²
Creator: Chancellor, Nicholas ³

¹ Durham University, United Kingdom
² dunnhumby, United Kingdom
³ Newcastle University, United Kingdom

Funder

Engineering and Physical Sciences Research Council
dunnhumby

Research methods

Computer assisted numerical calculations and quantum annealing experiments

Other description

Keyword

quantum computing
quantum annealing
combinatorial optimization

Subject

Quantum computing

Location

Language

English

Cited in

doi:10.48550/arXiv.2404.05476

Identifier

ark:/32150/r2j6731386t
doi:10.15128/r2j6731386t

Rights

Creative Commons Attribution 4.0 International (CC BY)

Publisher

Durham University

Date Created

2024-05-10

File Details

Depositor

P. Mirkarimi

Date Uploaded

10 May 2024, 11:05:16

Date Modified

13 May 2024, 10:05:36

Audit Status

Audits have not yet been run on this file.

Characterization

File format: zip (ZIP Format)

Mime type: application/zip

File size: 313808063

Last modified: 2024:05:10 12:50:23+01:00

Filename: ExperimentalLinearPenalties.zip

Original checksum: 70a242611e585620cf43058b9ce595b6