This title appears in the Scientific Report :
2022
Please use the identifier:
http://dx.doi.org/10.3389/fphy.2022.956882 in citations.
Please use the identifier: http://hdl.handle.net/2128/32203 in citations.
On the hardness of quadratic unconstrained binary optimization problems
On the hardness of quadratic unconstrained binary optimization problems
We use exact enumeration to characterize the solutions of quadratic unconstrained binary optimization problems of less than 21 variables in terms of their distributions of Hamming distances to close-by solutions. We also perform experiments with the D-Wave Advantage 5.1 quantum annealer, solving man...
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Personal Name(s): | Mehta, V. |
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Jin, F. / Michielsen, K. (Corresponding author) / De Raedt, H. | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Published in: | Frontiers in physics, 10 (2022) S. 956882 |
Imprint: |
Lausanne
Frontiers Media
2022
|
DOI: |
10.3389/fphy.2022.956882 |
Document Type: |
Journal Article |
Research Program: |
Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/32203 in citations.
We use exact enumeration to characterize the solutions of quadratic unconstrained binary optimization problems of less than 21 variables in terms of their distributions of Hamming distances to close-by solutions. We also perform experiments with the D-Wave Advantage 5.1 quantum annealer, solving many instances of up to 170-variable, quadratic unconstrained binary optimization problems. Our results demonstrate that the exponents characterizing the success probability of a D-Wave annealer to solve a quadratic unconstrained binary optimization correlate very well with the predictions based on the Hamming distance distributions computed for small problem instances. |