Print Email Facebook Twitter Quantum-Classical Solution Methods for Binary Compressive Sensing Problems Title Quantum-Classical Solution Methods for Binary Compressive Sensing Problems Author Wezeman, R.S. Chiscop, I. Anitori, L. van Rossum, W.L. Publication year 2022 Abstract 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. Subject 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 To reference this document use: http://resolver.tudelft.nl/uuid:3f852361-6a30-434b-a972-f32b258c0fe6 TNO identifier 973205 Publisher Springer Science and Business Media Deutschland GmbH ISBN 9783031087592 ISSN 0302-9743 Source 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, 107-121 Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.