Continuous-variable quantum approximate optimization on a programmable photonic quantum processor
Variational quantum algorithms (VQAs) provide a promising approach to achieving quantum advantage for practical problems on near-term noisy intermediate-scale quantum (NISQ) devices. Thus far, most studies on VQAs have focused on qubit-based systems, but the power of VQAs can be potentially boosted...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2023-10-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.5.043005 |
| _version_ | 1797210369863712768 |
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| author | Yutaro Enomoto Keitaro Anai Kenta Udagawa Shuntaro Takeda |
| author_facet | Yutaro Enomoto Keitaro Anai Kenta Udagawa Shuntaro Takeda |
| author_sort | Yutaro Enomoto |
| collection | DOAJ |
| description | Variational quantum algorithms (VQAs) provide a promising approach to achieving quantum advantage for practical problems on near-term noisy intermediate-scale quantum (NISQ) devices. Thus far, most studies on VQAs have focused on qubit-based systems, but the power of VQAs can be potentially boosted by exploiting infinite-dimensional continuous-variable (CV) systems. Here, we implement the CV version of one VQA, a quantum approximate optimization algorithm, by developing an automated collaborative computing system between a programmable photonic quantum computer and a classical computer. We experimentally demonstrate that this algorithm solves the minimization problem of simple continuous functions by implementing the quantum version of gradient descent to localize an initially broadly distributed wave function to the minimum. This method allows the execution of a practical CV quantum algorithm on a physical platform. Our work can be extended to the minimization of more general functions, providing an alternative to achieve the quantum advantage in practical problems. |
| first_indexed | 2024-04-24T10:09:30Z |
| format | Article |
| id | doaj.art-4de613176620496b9a29f68d1258bb7c |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:09:30Z |
| publishDate | 2023-10-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-4de613176620496b9a29f68d1258bb7c2024-04-12T17:34:36ZengAmerican Physical SocietyPhysical Review Research2643-15642023-10-015404300510.1103/PhysRevResearch.5.043005Continuous-variable quantum approximate optimization on a programmable photonic quantum processorYutaro EnomotoKeitaro AnaiKenta UdagawaShuntaro TakedaVariational quantum algorithms (VQAs) provide a promising approach to achieving quantum advantage for practical problems on near-term noisy intermediate-scale quantum (NISQ) devices. Thus far, most studies on VQAs have focused on qubit-based systems, but the power of VQAs can be potentially boosted by exploiting infinite-dimensional continuous-variable (CV) systems. Here, we implement the CV version of one VQA, a quantum approximate optimization algorithm, by developing an automated collaborative computing system between a programmable photonic quantum computer and a classical computer. We experimentally demonstrate that this algorithm solves the minimization problem of simple continuous functions by implementing the quantum version of gradient descent to localize an initially broadly distributed wave function to the minimum. This method allows the execution of a practical CV quantum algorithm on a physical platform. Our work can be extended to the minimization of more general functions, providing an alternative to achieve the quantum advantage in practical problems.http://doi.org/10.1103/PhysRevResearch.5.043005 |
| spellingShingle | Yutaro Enomoto Keitaro Anai Kenta Udagawa Shuntaro Takeda Continuous-variable quantum approximate optimization on a programmable photonic quantum processor Physical Review Research |
| title | Continuous-variable quantum approximate optimization on a programmable photonic quantum processor |
| title_full | Continuous-variable quantum approximate optimization on a programmable photonic quantum processor |
| title_fullStr | Continuous-variable quantum approximate optimization on a programmable photonic quantum processor |
| title_full_unstemmed | Continuous-variable quantum approximate optimization on a programmable photonic quantum processor |
| title_short | Continuous-variable quantum approximate optimization on a programmable photonic quantum processor |
| title_sort | continuous variable quantum approximate optimization on a programmable photonic quantum processor |
| url | http://doi.org/10.1103/PhysRevResearch.5.043005 |
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