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Fluctuation guided search in quantum annealing [dataset and code] Open Access

Quantum annealing has great promise in leveraging quantum mechanics to solve combinatorial optimisation problems. However, to realize this promise to it's fullest extent we must appropriately leverage the underlying physics. In this spirit, I examine how the well known tendency of quantum annealers to seek solutions where higher levels of quantum fluctuations are present can be used to trade off optimality of the solution to a synthetic problem for the ability to have a more flexible solution, where some variables can be changed at little or no cost. I demonstrate this tradeoff experimentally using the reverse annealing feature a D-Wave Systems QPU for both problems composed of all binary variables, and those containing some higher-than-binary discrete variables. I further demonstrate how local controls on the qubits can be used to control the levels of fluctuations and guide the search. I discuss places where leveraging this tradeoff could be practically important, namely in hybrid algorithms where some penalties cannot be directly implemented on the annealer and provide some proof-of-concept evidence of how these algorithms could work.

Descriptions

Resource type

Dataset

Contributors

Creator: Chancellor, Nicholas ¹
Contact person: Chancellor, Nicholas ¹
Data collector: Chancellor, Nicholas ¹
Data curator: Chancellor, Nicholas ¹

¹ Durham University, United Kingdom

Funder

Engineering and Physical Sciences Research Council
BP

Research methods

Remote quantum annealing experiments

Other description

data and code associated with  https://arxiv.org/abs/2009.06335

Keyword

Quantum annealing
D-Wave

Subject

Location

Language

Matlab
Python

Cited in

arxiv:2009.06335

Identifier

ark:/32150/r1c534fn95w
doi:10.15128/r1c534fn95w

Rights

Creative Commons Attribution 4.0 International (CC BY)

Publisher

Durham University

Date Created

2020-09-23

File Details

Depositor

N.G. Chancellor

Date Uploaded

23 September 2020, 20:09:08

Date Modified

25 September 2020, 11:09:41

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Characterization

File format: zip (ZIP Format)

Mime type: application/zip

File size: 13022471355

Last modified: 2020:09:23 21:59:32+01:00

Filename: fluctuation_guided_repo.zip

Original checksum: ec37a6e6d51cf27c55d1e4dfb7c29630