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 sensing
Quadratic unconstrained binary optimisation
Quantum annealing
Computational complexity
Linear systems
Optimization
Quantum computers
Quantum optics
Binary compressive sensing
Binary optimization
Classical solutions
Compressive sensing
Quadratic unconstrained binary optimization
Quantum annealing
Quantum-classical
Sensing problems
Signal processing technique
Solution methods
Compressed 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