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Quantum optimization with linear Ising penalty functions for customer data science [dataset] Open Access

Constrained combinatorial optimization problems, which are ubiquitous in industry, can be solved by quantum algorithms such as quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA). In these quantum algorithms, constraints are typically implemented with quadratic penalty functions. This penalty method can introduce large energy scales and make interaction graphs much more dense. These effects can result in worse performance of quantum optimization, particularly on near-term devices that have sparse hardware graphs and other physical limitations. In this work, we consider linear Ising penalty functions, which are applied with local fields in the Ising model, as an alternative method for implementing constraints that makes more efficient use of physical resources. We study the behaviour of the penalty method in the context of quantum optimization for customer data science problems. Our theoretical analysis and numerical simulations of QA and the QAOA indicate that this penalty method can lead to better performance in quantum optimization than the quadratic method. However, the linear Ising penalty method is not suitable for all problems as it cannot always exactly implement the desired constraint. In cases where the linear method is not successful in implementing all constraints, we propose that schemes involving both quadratic and linear Ising penalties can be effective.

Descriptions

Resource type
Dataset
Contributors
Data collector: Mirkarimi, Puya 1
Creator: Mirkarimi, Puya 1
Contact person: Mirkarimi, Puya 1
Editor: Mirkarimi, Puya 1
Data curator: Mirkarimi, Puya 1
Data collector: Shukla, Ishaan 1
Creator: Shukla, Ishaan 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
Other description
Keyword
quantum computing
quantum annealing
quantum approximate optimization algorithm
combinatorial optimization
Subject
Quantum computing
Location
Language
English
Cited in
doi:10.48550/arXiv.2404.05467
Identifier
ark:/32150/r2fq977t82m
doi:10.15128/r2fq977t82m
Rights
Creative Commons Attribution 4.0 International (CC BY)

Publisher
Durham University
Date Created
2024-05-10

File Details

Depositor
P. Mirkarimi
Date Uploaded
Date Modified
13 May 2024, 10:05:14
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Characterization
File format: zip (ZIP Format)
Mime type: application/zip
File size: 275404604
Last modified: 2024:05:10 12:31:14+01:00
Filename: QuantumLinearPenalty.zip
Original checksum: cb8e7501e92b2d9084849a0cb7b184a7
Activity of users you follow
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User P. Mirkarimi has updated Quantum optimization with linear Ising penalty functions for customer data science [dataset] about 1 month ago
User P. Mirkarimi has deposited QuantumLinearPenalty.zip about 1 month ago