Q-Gen: A Parameterized Quantum Circuit Generator
Unlike most classical algorithms that take an input and give the solution directly as an output, quantum algorithms produce a quantum circuit that works as an indirect solution to computationally hard problems. In the full quantum computing workflow, most data processing remains in the classical dom...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/11008486/ |
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| author | Yikai Mao Shaswot Shresthamali Masaaki Kondo |
| author_facet | Yikai Mao Shaswot Shresthamali Masaaki Kondo |
| author_sort | Yikai Mao |
| collection | DOAJ |
| description | Unlike most classical algorithms that take an input and give the solution directly as an output, quantum algorithms produce a quantum circuit that works as an indirect solution to computationally hard problems. In the full quantum computing workflow, most data processing remains in the classical domain except for running the quantum circuit in the quantum processor. This leaves massive opportunities for classical automation and optimization toward future utilization of quantum computing. We kick-start the first step in this direction by introducing Q-gen, a high-level parameterized quantum circuit generator incorporating 15 realistic quantum algorithms. Each customized generation function comes with algorithm-specific parameters beyond the number of qubits, providing a large generation volume with high circuit variability. To demonstrate the functionality of Q-gen, we organize the algorithms into five hierarchical systems and generate a quantum circuit dataset accompanied by their measurement histograms and state vectors. This dataset enables researchers to statistically analyze the structure, complexity, and performance of large-scale quantum circuits or quickly train novel machine learning models without worrying about the exponentially growing simulation time. Q-gen is an open-source and multipurpose project that serves as the entrance for users with a classical computer science background to dive into the world of quantum computing. |
| format | Article |
| id | doaj-art-706558df25024d848cc493681874409a |
| institution | OA Journals |
| issn | 2689-1808 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-706558df25024d848cc493681874409a2025-08-20T02:36:59ZengIEEEIEEE Transactions on Quantum Engineering2689-18082025-01-01611610.1109/TQE.2025.357214211008486Q-Gen: A Parameterized Quantum Circuit GeneratorYikai Mao0https://orcid.org/0009-0008-4182-3173Shaswot Shresthamali1https://orcid.org/0000-0001-8965-1018Masaaki Kondo2https://orcid.org/0000-0002-6025-8738Graduate School of Science and Technology, Keio University, Yokohama, JapanGraduate School of Science and Technology, Keio University, Yokohama, JapanGraduate School of Science and Technology, Keio University, Yokohama, JapanUnlike most classical algorithms that take an input and give the solution directly as an output, quantum algorithms produce a quantum circuit that works as an indirect solution to computationally hard problems. In the full quantum computing workflow, most data processing remains in the classical domain except for running the quantum circuit in the quantum processor. This leaves massive opportunities for classical automation and optimization toward future utilization of quantum computing. We kick-start the first step in this direction by introducing Q-gen, a high-level parameterized quantum circuit generator incorporating 15 realistic quantum algorithms. Each customized generation function comes with algorithm-specific parameters beyond the number of qubits, providing a large generation volume with high circuit variability. To demonstrate the functionality of Q-gen, we organize the algorithms into five hierarchical systems and generate a quantum circuit dataset accompanied by their measurement histograms and state vectors. This dataset enables researchers to statistically analyze the structure, complexity, and performance of large-scale quantum circuits or quickly train novel machine learning models without worrying about the exponentially growing simulation time. Q-gen is an open-source and multipurpose project that serves as the entrance for users with a classical computer science background to dive into the world of quantum computing.https://ieeexplore.ieee.org/document/11008486/Quantum algorithmquantum circuitquantum simulation |
| spellingShingle | Yikai Mao Shaswot Shresthamali Masaaki Kondo Q-Gen: A Parameterized Quantum Circuit Generator IEEE Transactions on Quantum Engineering Quantum algorithm quantum circuit quantum simulation |
| title | Q-Gen: A Parameterized Quantum Circuit Generator |
| title_full | Q-Gen: A Parameterized Quantum Circuit Generator |
| title_fullStr | Q-Gen: A Parameterized Quantum Circuit Generator |
| title_full_unstemmed | Q-Gen: A Parameterized Quantum Circuit Generator |
| title_short | Q-Gen: A Parameterized Quantum Circuit Generator |
| title_sort | q gen a parameterized quantum circuit generator |
| topic | Quantum algorithm quantum circuit quantum simulation |
| url | https://ieeexplore.ieee.org/document/11008486/ |
| work_keys_str_mv | AT yikaimao qgenaparameterizedquantumcircuitgenerator AT shaswotshresthamali qgenaparameterizedquantumcircuitgenerator AT masaakikondo qgenaparameterizedquantumcircuitgenerator |