Variational quantum compiling with double Q-learning
Quantum compiling aims to construct a quantum circuit V by quantum gates drawn from a native gate alphabet, which is functionally equivalent to the target unitary U . It is a crucial stage for the running of quantum algorithms on noisy intermediate-scale quantum (NISQ) devices. However, the space fo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2021-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/abe0ae |
| _version_ | 1797750149697503232 |
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| author | Zhimin He Lvzhou Li Shenggen Zheng Yongyao Li Haozhen Situ |
| author_facet | Zhimin He Lvzhou Li Shenggen Zheng Yongyao Li Haozhen Situ |
| author_sort | Zhimin He |
| collection | DOAJ |
| description | Quantum compiling aims to construct a quantum circuit V by quantum gates drawn from a native gate alphabet, which is functionally equivalent to the target unitary U . It is a crucial stage for the running of quantum algorithms on noisy intermediate-scale quantum (NISQ) devices. However, the space for structure exploration of quantum circuit is enormous, resulting in the requirement of human expertise, hundreds of experimentations or modifications from existing quantum circuits. In this paper, we propose a variational quantum compiling (VQC) algorithm based on reinforcement learning, in order to automatically design the structure of quantum circuit for VQC with no human intervention. An agent is trained to sequentially select quantum gates from the native gate alphabet and the qubits they act on by double Q -learning with ϵ -greedy exploration strategy and experience replay. At first, the agent randomly explores a number of quantum circuits with different structures, and then iteratively discovers structures with higher performance on the learning task. Simulation results show that the proposed method can make exact compilations with less quantum gates compared to previous VQC algorithms. It can reduce the errors of quantum algorithms due to decoherence process and gate noise in NISQ devices, and enable quantum algorithms especially for complex algorithms to be executed within coherence time. |
| first_indexed | 2024-03-12T16:29:32Z |
| format | Article |
| id | doaj.art-7df405cb25bf47d0a77a564780c198aa |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:29:32Z |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-7df405cb25bf47d0a77a564780c198aa2023-08-08T15:34:21ZengIOP PublishingNew Journal of Physics1367-26302021-01-0123303300210.1088/1367-2630/abe0aeVariational quantum compiling with double Q-learningZhimin He0https://orcid.org/0000-0002-1684-1758Lvzhou Li1Shenggen Zheng2Yongyao Li3https://orcid.org/0000-0003-3067-4762Haozhen Situ4https://orcid.org/0000-0001-7853-6647School of Electronic and Information Engineering, Foshan University , Foshan 528000, People’s Republic of China; Peng Cheng Laboratory , Shenzhen 518055, People’s Republic of ChinaInstitute of Quantum Computing and Computer Science Theory, School of Computer Science and Engineering, Sun Yat-Sen University , Guangzhou 510006, People’s Republic of ChinaPeng Cheng Laboratory , Shenzhen 518055, People’s Republic of ChinaSchool of Physics and Optoelectronic Engineering, Foshan University , Foshan 528000, People’s Republic of ChinaCollege of Mathematics and Informatics, South China Agricultural University , Guangzhou 510642, People’s Republic of ChinaQuantum compiling aims to construct a quantum circuit V by quantum gates drawn from a native gate alphabet, which is functionally equivalent to the target unitary U . It is a crucial stage for the running of quantum algorithms on noisy intermediate-scale quantum (NISQ) devices. However, the space for structure exploration of quantum circuit is enormous, resulting in the requirement of human expertise, hundreds of experimentations or modifications from existing quantum circuits. In this paper, we propose a variational quantum compiling (VQC) algorithm based on reinforcement learning, in order to automatically design the structure of quantum circuit for VQC with no human intervention. An agent is trained to sequentially select quantum gates from the native gate alphabet and the qubits they act on by double Q -learning with ϵ -greedy exploration strategy and experience replay. At first, the agent randomly explores a number of quantum circuits with different structures, and then iteratively discovers structures with higher performance on the learning task. Simulation results show that the proposed method can make exact compilations with less quantum gates compared to previous VQC algorithms. It can reduce the errors of quantum algorithms due to decoherence process and gate noise in NISQ devices, and enable quantum algorithms especially for complex algorithms to be executed within coherence time.https://doi.org/10.1088/1367-2630/abe0aevariational quantum compilingreinforcement learningdouble Q-learning |
| spellingShingle | Zhimin He Lvzhou Li Shenggen Zheng Yongyao Li Haozhen Situ Variational quantum compiling with double Q-learning New Journal of Physics variational quantum compiling reinforcement learning double Q-learning |
| title | Variational quantum compiling with double Q-learning |
| title_full | Variational quantum compiling with double Q-learning |
| title_fullStr | Variational quantum compiling with double Q-learning |
| title_full_unstemmed | Variational quantum compiling with double Q-learning |
| title_short | Variational quantum compiling with double Q-learning |
| title_sort | variational quantum compiling with double q learning |
| topic | variational quantum compiling reinforcement learning double Q-learning |
| url | https://doi.org/10.1088/1367-2630/abe0ae |
| work_keys_str_mv | AT zhiminhe variationalquantumcompilingwithdoubleqlearning AT lvzhouli variationalquantumcompilingwithdoubleqlearning AT shenggenzheng variationalquantumcompilingwithdoubleqlearning AT yongyaoli variationalquantumcompilingwithdoubleqlearning AT haozhensitu variationalquantumcompilingwithdoubleqlearning |