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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| Vydáno v: | Applied sciences Ročník 14; číslo 18; s. 8099 |
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01.09.2024
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| ISSN: | 2076-3417, 2076-3417 |
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| Abstract | 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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| AbstractList | 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. |
| Audience | Academic |
| Author | Pecyna, Tomasz Różycki, Rafał |
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| Cites_doi | 10.1007/BF01343193 10.1016/j.ejor.2023.03.013 10.1137/S0036144598347011 10.1103/PhysRevResearch.4.033029 10.1287/trsc.2021.1056 10.1145/237814.237866 10.1145/3478519 10.1145/3665225.3665446 10.1007/978-3-031-63778-0_22 10.1103/PhysRevResearch.5.023171 10.22331/q-2018-08-06-79 10.1090/S0002-9939-1959-0108732-6 10.1007/978-94-015-8330-5_4 10.1016/j.physrep.2024.03.002 10.1103/PhysRevA.88.062314 10.1103/PhysRevA.103.042405 |
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| SubjectTerms | Algorithms Energy Hamiltonian Mathematical optimization Optimization algorithms quadratic unconstrained binary optimization quantum approximate optimization algorithm Quantum computing quantum optimization tactical aircraft deconfliction problem |
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| Title | Improving Quantum Optimization Algorithms by Constraint Relaxation |
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