Enhancing computational accuracy with parallel parameter optimization in variational quantum eigensolver
Variational quantum algorithms have promising applications in noisy intermediate-scale quantum (NISQ) devices. These algorithms rely on a classical optimization outer loop that minimizes a parameterized quantum circuit function. The optimization in variational quantum eigensolver (VQE) is NP-hard, m...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-01-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0236028 |
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| author | Daisuke Tsukayama Jun-ichi Shirakashi Tetsuo Shibuya Hiroshi Imai |
| author_facet | Daisuke Tsukayama Jun-ichi Shirakashi Tetsuo Shibuya Hiroshi Imai |
| author_sort | Daisuke Tsukayama |
| collection | DOAJ |
| description | Variational quantum algorithms have promising applications in noisy intermediate-scale quantum (NISQ) devices. These algorithms rely on a classical optimization outer loop that minimizes a parameterized quantum circuit function. The optimization in variational quantum eigensolver (VQE) is NP-hard, meaning that finding the optimal solution is infeasible in the worst-case scenario. One way to address this challenge is through parallel optimization of parameters using multiple-parameterized quantum circuits. However, this approach is unsuitable for cloud-based quantum processing unit utilization due to the increased number of quantum circuit executions. Although NISQ devices have limitations in terms of gate depth, their size has been growing in recent years. Therefore, implementing multiple-parameterized quantum circuits in NISQ devices can suppress the increase in the number of executions. In this study, we propose a parallel-VQE, which leverages the parallel execution of parameterized quantum circuits to perform parallel parameter optimization in VQE, achieving convergence to solutions closer to the ground state. We validate the effectiveness of parallel-VQE in solving the random weighted max-cut problem using numerical simulations and a real quantum device. We present the results of running up to six circuits in parallel (120 qubits) and demonstrate the advantages of using multiple units to improve computational accuracy. This study provides a potential method for solving eigenvalue problems and combinatorial optimization problems for future quantum devices. |
| format | Article |
| id | doaj-art-25ffbc63280344078078358932b86196 |
| institution | Kabale University |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-25ffbc63280344078078358932b861962025-02-03T16:40:42ZengAIP Publishing LLCAIP Advances2158-32262025-01-01151015226015226-2010.1063/5.0236028Enhancing computational accuracy with parallel parameter optimization in variational quantum eigensolverDaisuke Tsukayama0Jun-ichi Shirakashi1Tetsuo Shibuya2Hiroshi Imai3Department of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Koganei, Tokyo 184-8588, JapanDepartment of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Koganei, Tokyo 184-8588, JapanDivision of Medical Data Informatics, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato, Tokyo 108-8639, JapanThe Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo, Tokyo 113-8656, JapanVariational quantum algorithms have promising applications in noisy intermediate-scale quantum (NISQ) devices. These algorithms rely on a classical optimization outer loop that minimizes a parameterized quantum circuit function. The optimization in variational quantum eigensolver (VQE) is NP-hard, meaning that finding the optimal solution is infeasible in the worst-case scenario. One way to address this challenge is through parallel optimization of parameters using multiple-parameterized quantum circuits. However, this approach is unsuitable for cloud-based quantum processing unit utilization due to the increased number of quantum circuit executions. Although NISQ devices have limitations in terms of gate depth, their size has been growing in recent years. Therefore, implementing multiple-parameterized quantum circuits in NISQ devices can suppress the increase in the number of executions. In this study, we propose a parallel-VQE, which leverages the parallel execution of parameterized quantum circuits to perform parallel parameter optimization in VQE, achieving convergence to solutions closer to the ground state. We validate the effectiveness of parallel-VQE in solving the random weighted max-cut problem using numerical simulations and a real quantum device. We present the results of running up to six circuits in parallel (120 qubits) and demonstrate the advantages of using multiple units to improve computational accuracy. This study provides a potential method for solving eigenvalue problems and combinatorial optimization problems for future quantum devices.http://dx.doi.org/10.1063/5.0236028 |
| spellingShingle | Daisuke Tsukayama Jun-ichi Shirakashi Tetsuo Shibuya Hiroshi Imai Enhancing computational accuracy with parallel parameter optimization in variational quantum eigensolver AIP Advances |
| title | Enhancing computational accuracy with parallel parameter optimization in variational quantum eigensolver |
| title_full | Enhancing computational accuracy with parallel parameter optimization in variational quantum eigensolver |
| title_fullStr | Enhancing computational accuracy with parallel parameter optimization in variational quantum eigensolver |
| title_full_unstemmed | Enhancing computational accuracy with parallel parameter optimization in variational quantum eigensolver |
| title_short | Enhancing computational accuracy with parallel parameter optimization in variational quantum eigensolver |
| title_sort | enhancing computational accuracy with parallel parameter optimization in variational quantum eigensolver |
| url | http://dx.doi.org/10.1063/5.0236028 |
| work_keys_str_mv | AT daisuketsukayama enhancingcomputationalaccuracywithparallelparameteroptimizationinvariationalquantumeigensolver AT junichishirakashi enhancingcomputationalaccuracywithparallelparameteroptimizationinvariationalquantumeigensolver AT tetsuoshibuya enhancingcomputationalaccuracywithparallelparameteroptimizationinvariationalquantumeigensolver AT hiroshiimai enhancingcomputationalaccuracywithparallelparameteroptimizationinvariationalquantumeigensolver |