Quantum Program Synthesis Through Operator Learning and Selection
Programming for quantum computers is complicated and time-consuming, because quantum operations are counterintuitive and their combined effects are difficult to understand. Existing tools allow automatic synthesis of quantum programs, which releases the burden of handwriting. However, many existing...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10068537/ |
| _version_ | 1797866987500601344 |
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| author | Sihyung Lee Seung Yeob Nam |
| author_facet | Sihyung Lee Seung Yeob Nam |
| author_sort | Sihyung Lee |
| collection | DOAJ |
| description | Programming for quantum computers is complicated and time-consuming, because quantum operations are counterintuitive and their combined effects are difficult to understand. Existing tools allow automatic synthesis of quantum programs, which releases the burden of handwriting. However, many existing systems arrange predetermined operators in successive manner to gradually reduce the gap with requirements; these methods are quick but often produce lengthy programs, and they are difficult to adopt for new operators. Other systems depend on stochastic or heuristic search; they identify near-optimal programs for certain cases, but it is not easy to tune the algorithms for a wide range of cases. We propose a system that produces compact programs for most cases and easily evolves with new operators. The system automatically learns the roles of available operators by composing various possible programs. Based on the knowledge, it selects a subset of operators most appropriate for requirements and uses them to compose a program. The learning is geared toward concise programs; thus, the system tends to produce programs with the fewest operators possible. We implemented the system and evaluated it by synthesizing over 400 programs. In comparison with a state-of-the-art system, the proposed system produced programs with approximately 40-times fewer operators at the cost of increased synthesis time from seconds to minutes. We also observed that the system successfully adopted new operators by learning their differences from existing operators and utilizing them in right places. We believe that the system provides a basis of utilizing machine learning for quantum program synthesis. |
| first_indexed | 2024-04-09T23:34:06Z |
| format | Article |
| id | doaj.art-8ae24ee38a8543b7a5142af3f0b63056 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T23:34:06Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8ae24ee38a8543b7a5142af3f0b630562023-03-20T23:00:19ZengIEEEIEEE Access2169-35362023-01-0111257552576710.1109/ACCESS.2023.325719210068537Quantum Program Synthesis Through Operator Learning and SelectionSihyung Lee0https://orcid.org/0000-0001-7945-3763Seung Yeob Nam1https://orcid.org/0000-0001-8249-4742School of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaProgramming for quantum computers is complicated and time-consuming, because quantum operations are counterintuitive and their combined effects are difficult to understand. Existing tools allow automatic synthesis of quantum programs, which releases the burden of handwriting. However, many existing systems arrange predetermined operators in successive manner to gradually reduce the gap with requirements; these methods are quick but often produce lengthy programs, and they are difficult to adopt for new operators. Other systems depend on stochastic or heuristic search; they identify near-optimal programs for certain cases, but it is not easy to tune the algorithms for a wide range of cases. We propose a system that produces compact programs for most cases and easily evolves with new operators. The system automatically learns the roles of available operators by composing various possible programs. Based on the knowledge, it selects a subset of operators most appropriate for requirements and uses them to compose a program. The learning is geared toward concise programs; thus, the system tends to produce programs with the fewest operators possible. We implemented the system and evaluated it by synthesizing over 400 programs. In comparison with a state-of-the-art system, the proposed system produced programs with approximately 40-times fewer operators at the cost of increased synthesis time from seconds to minutes. We also observed that the system successfully adopted new operators by learning their differences from existing operators and utilizing them in right places. We believe that the system provides a basis of utilizing machine learning for quantum program synthesis.https://ieeexplore.ieee.org/document/10068537/Machine learningneural networksprogram synthesisquantum computingquantum programsupervised learning |
| spellingShingle | Sihyung Lee Seung Yeob Nam Quantum Program Synthesis Through Operator Learning and Selection IEEE Access Machine learning neural networks program synthesis quantum computing quantum program supervised learning |
| title | Quantum Program Synthesis Through Operator Learning and Selection |
| title_full | Quantum Program Synthesis Through Operator Learning and Selection |
| title_fullStr | Quantum Program Synthesis Through Operator Learning and Selection |
| title_full_unstemmed | Quantum Program Synthesis Through Operator Learning and Selection |
| title_short | Quantum Program Synthesis Through Operator Learning and Selection |
| title_sort | quantum program synthesis through operator learning and selection |
| topic | Machine learning neural networks program synthesis quantum computing quantum program supervised learning |
| url | https://ieeexplore.ieee.org/document/10068537/ |
| work_keys_str_mv | AT sihyunglee quantumprogramsynthesisthroughoperatorlearningandselection AT seungyeobnam quantumprogramsynthesisthroughoperatorlearningandselection |