Performance analysis of a filtering variational quantum algorithm
Even a minor boost in solving combinatorial optimization problems can greatly benefit multiple industries. Quantum computers, with their unique information processing capabilities, hold promise for delivering such enhancements. The filtering variational quantum eigensolver (F-VQE) is a variational h...
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IOP Publishing
2025-01-01
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| Series: | New Journal of Physics |
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| Online Access: | https://doi.org/10.1088/1367-2630/add365 |
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| author | Gabriel Marin-Sanchez David Amaro |
| author_facet | Gabriel Marin-Sanchez David Amaro |
| author_sort | Gabriel Marin-Sanchez |
| collection | DOAJ |
| description | Even a minor boost in solving combinatorial optimization problems can greatly benefit multiple industries. Quantum computers, with their unique information processing capabilities, hold promise for delivering such enhancements. The filtering variational quantum eigensolver (F-VQE) is a variational hybrid quantum algorithm designed to solve combinatorial optimization problems on existing quantum computers with limited qubit number, connectivity, and fidelity. In this work we employ instantaneous quantum polynomial circuits as our parameterized quantum circuits. We propose a hardware-efficient implementation that respects limited qubit connectivity and show that they halve the number of circuits necessary to evaluate the gradient with the parameter-shift rule. To assess the potential of this protocol in the context of combinatorial optimization, we conduct extensive numerical analysis. We compare the performance against three classical baseline algorithms on weighted MaxCut and the asymmetric traveling salesperson problem (ATSP). We employ noiseless simulators for problems encoded on 13–29 qubits, and up to 37 qubits on the IBMQ real quantum devices. The ATSP encoding employed reduces the number of qubits and avoids the need of constraints compared to the standard quadratic unconstrained binary optimization/Ising model. Despite some observed positive signs, we conclude that significant development is necessary for a practical advantage with F-VQE. |
| format | Article |
| id | doaj-art-070d067a5f8349a9ac3fd45fa9722cc8 |
| institution | Kabale University |
| issn | 1367-2630 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj-art-070d067a5f8349a9ac3fd45fa9722cc82025-08-20T03:52:56ZengIOP PublishingNew Journal of Physics1367-26302025-01-0127505450510.1088/1367-2630/add365Performance analysis of a filtering variational quantum algorithmGabriel Marin-Sanchez0https://orcid.org/0009-0003-7630-6081David Amaro1https://orcid.org/0000-0001-7853-9581Quantinuum, Partnership House , Carlisle Place, London SW1P 1BX, United KingdomQuantinuum, Partnership House , Carlisle Place, London SW1P 1BX, United KingdomEven a minor boost in solving combinatorial optimization problems can greatly benefit multiple industries. Quantum computers, with their unique information processing capabilities, hold promise for delivering such enhancements. The filtering variational quantum eigensolver (F-VQE) is a variational hybrid quantum algorithm designed to solve combinatorial optimization problems on existing quantum computers with limited qubit number, connectivity, and fidelity. In this work we employ instantaneous quantum polynomial circuits as our parameterized quantum circuits. We propose a hardware-efficient implementation that respects limited qubit connectivity and show that they halve the number of circuits necessary to evaluate the gradient with the parameter-shift rule. To assess the potential of this protocol in the context of combinatorial optimization, we conduct extensive numerical analysis. We compare the performance against three classical baseline algorithms on weighted MaxCut and the asymmetric traveling salesperson problem (ATSP). We employ noiseless simulators for problems encoded on 13–29 qubits, and up to 37 qubits on the IBMQ real quantum devices. The ATSP encoding employed reduces the number of qubits and avoids the need of constraints compared to the standard quadratic unconstrained binary optimization/Ising model. Despite some observed positive signs, we conclude that significant development is necessary for a practical advantage with F-VQE.https://doi.org/10.1088/1367-2630/add365IQPQMLquantum optimizationvariational algorithm |
| spellingShingle | Gabriel Marin-Sanchez David Amaro Performance analysis of a filtering variational quantum algorithm New Journal of Physics IQP QML quantum optimization variational algorithm |
| title | Performance analysis of a filtering variational quantum algorithm |
| title_full | Performance analysis of a filtering variational quantum algorithm |
| title_fullStr | Performance analysis of a filtering variational quantum algorithm |
| title_full_unstemmed | Performance analysis of a filtering variational quantum algorithm |
| title_short | Performance analysis of a filtering variational quantum algorithm |
| title_sort | performance analysis of a filtering variational quantum algorithm |
| topic | IQP QML quantum optimization variational algorithm |
| url | https://doi.org/10.1088/1367-2630/add365 |
| work_keys_str_mv | AT gabrielmarinsanchez performanceanalysisofafilteringvariationalquantumalgorithm AT davidamaro performanceanalysisofafilteringvariationalquantumalgorithm |