Quantum-Classical Solution Methods for Binary Compressive Sensing Problems
conference paper
Compressive sensing is a signal processing technique used to acquire and reconstruct sparse signals using significantly fewer measurement samples. Compressive sensing requires finding the most sparse solution to an underdetermined linear system, which is an NP-hard problem and as a consequence in practise is only solved approximately. In our work we restrict ourselves to the compressive sensing problem for the case of binary signals. For that case we have defined an equivalent formulation in terms of a quadratic binary optimisation (QUBO) problem, which we solve using classical and (hybrid--)quantum computing solving techniques based on quantum annealing. Phase transition diagrams show that this approach significantly improves the number of problem types that can be successfully reconstructed when compared to a more conventional L1 optimisation method. A challenge that remain is how to select optimal penalty parameters in the QUBO formulation as was shown can heavily impact the quality of the solution.
Topics
Binary compressive sensingQuadratic unconstrained binary optimisationQuantum annealingComputational complexityLinear systemsOptimizationQuantum computersQuantum opticsBinary compressive sensingBinary optimizationClassical solutionsCompressive sensingQuadratic unconstrained binary optimizationQuantum annealingQuantum-classicalSensing problemsSignal processing techniqueSolution methodsCompressed sensing
TNO Identifier
973205
ISSN
03029743
ISBN
9783031087592
Publisher
Springer Science and Business Media Deutschland GmbH
Source title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 22nd Annual International Conference on Computational Science, ICCS 2022, 21 June 2022 through 23 June 2022
Pages
107-121
Files
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