Quantum approximate optimization via learning-based adaptive optimization
Abstract Combinatorial optimization problems are ubiquitous and computationally hard to solve in general. Quantum approximate optimization algorithm (QAOA), one of the most representative quantum-classical hybrid algorithms, is designed to solve combinatorial optimization problems by transforming th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-024-01577-x |
| _version_ | 1797266793294725120 |
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| author | Lixue Cheng Yu-Qin Chen Shi-Xin Zhang Shengyu Zhang |
| author_facet | Lixue Cheng Yu-Qin Chen Shi-Xin Zhang Shengyu Zhang |
| author_sort | Lixue Cheng |
| collection | DOAJ |
| description | Abstract Combinatorial optimization problems are ubiquitous and computationally hard to solve in general. Quantum approximate optimization algorithm (QAOA), one of the most representative quantum-classical hybrid algorithms, is designed to solve combinatorial optimization problems by transforming the discrete optimization problem into a classical optimization problem over continuous circuit parameters. QAOA objective landscape is notorious for pervasive local minima, and its viability significantly relies on the efficacy of the classical optimizer. In this work, we design double adaptive-region Bayesian optimization (DARBO) for QAOA. Our numerical results demonstrate that the algorithm greatly outperforms conventional optimizers in terms of speed, accuracy, and stability. We also address the issues of measurement efficiency and the suppression of quantum noise by conducting the full optimization loop on a superconducting quantum processor as a proof of concept. This work helps to unlock the full power of QAOA and paves the way toward achieving quantum advantage in practical classical tasks. |
| first_indexed | 2024-04-25T01:06:20Z |
| format | Article |
| id | doaj.art-caa945f3d8fa4e31bf794c8482036df3 |
| institution | Directory Open Access Journal |
| issn | 2399-3650 |
| language | English |
| last_indexed | 2024-04-25T01:06:20Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Physics |
| spelling | doaj.art-caa945f3d8fa4e31bf794c8482036df32024-03-10T12:14:24ZengNature PortfolioCommunications Physics2399-36502024-03-01711910.1038/s42005-024-01577-xQuantum approximate optimization via learning-based adaptive optimizationLixue Cheng0Yu-Qin Chen1Shi-Xin Zhang2Shengyu Zhang3Tencent Quantum LaboratoryTencent Quantum LaboratoryTencent Quantum LaboratoryTencent Quantum LaboratoryAbstract Combinatorial optimization problems are ubiquitous and computationally hard to solve in general. Quantum approximate optimization algorithm (QAOA), one of the most representative quantum-classical hybrid algorithms, is designed to solve combinatorial optimization problems by transforming the discrete optimization problem into a classical optimization problem over continuous circuit parameters. QAOA objective landscape is notorious for pervasive local minima, and its viability significantly relies on the efficacy of the classical optimizer. In this work, we design double adaptive-region Bayesian optimization (DARBO) for QAOA. Our numerical results demonstrate that the algorithm greatly outperforms conventional optimizers in terms of speed, accuracy, and stability. We also address the issues of measurement efficiency and the suppression of quantum noise by conducting the full optimization loop on a superconducting quantum processor as a proof of concept. This work helps to unlock the full power of QAOA and paves the way toward achieving quantum advantage in practical classical tasks.https://doi.org/10.1038/s42005-024-01577-x |
| spellingShingle | Lixue Cheng Yu-Qin Chen Shi-Xin Zhang Shengyu Zhang Quantum approximate optimization via learning-based adaptive optimization Communications Physics |
| title | Quantum approximate optimization via learning-based adaptive optimization |
| title_full | Quantum approximate optimization via learning-based adaptive optimization |
| title_fullStr | Quantum approximate optimization via learning-based adaptive optimization |
| title_full_unstemmed | Quantum approximate optimization via learning-based adaptive optimization |
| title_short | Quantum approximate optimization via learning-based adaptive optimization |
| title_sort | quantum approximate optimization via learning based adaptive optimization |
| url | https://doi.org/10.1038/s42005-024-01577-x |
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