Improving Quantum Optimization Algorithms by Constraint Relaxation

Quantum optimization is a significant area of quantum computing research with anticipated near-term quantum advantages. Current quantum optimization algorithms, most of which are hybrid variational-Hamiltonian-based algorithms, struggle to present quantum devices due to noise and decoherence. Existi...

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Bibliographic Details
Published in:Applied sciences Vol. 14; no. 18; p. 8099
Main Authors: Pecyna, Tomasz, Różycki, Rafał
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2024
Subjects:
Algorithms
Energy
Hamiltonian
Mathematical optimization
Optimization algorithms
quadratic unconstrained binary optimization
quantum approximate optimization algorithm
Quantum computing
quantum optimization
tactical aircraft deconfliction problem
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:Quantum optimization is a significant area of quantum computing research with anticipated near-term quantum advantages. Current quantum optimization algorithms, most of which are hybrid variational-Hamiltonian-based algorithms, struggle to present quantum devices due to noise and decoherence. Existing techniques attempt to mitigate these issues through employing different Hamiltonian encodings or Hamiltonian clause pruning, but they often rely on optimistic assumptions rather than a deep analysis of the problem structure. We demonstrate how to formulate the problem Hamiltonian for a quantum approximate optimization algorithm that satisfies all the requirements to correctly describe the considered tactical aircraft deconfliction problem, achieving higher probabilities for finding solutions compared to previous works. Our results indicate that constructing Hamiltonians from an unconventional, quantum-specific perspective with a high degree of entanglement results in a linear instead of exponential number of entanglement gates instead and superior performance compared to standard formulations. Specifically, we achieve a higher probability of finding feasible solutions: finding solutions in nine out of nine instances compared to standard Hamiltonian formulations and quadratic programming formulations known from quantum annealers, which only found solutions in seven out of nine instances. These findings suggest that there is substantial potential for further research in quantum Hamiltonian design and that gate-based approaches may offer superior optimization performance over quantum annealers in the future.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app14188099