VQE-generated quantum circuit dataset for machine learning
Quantum machine learning has the potential to computationally outperform classical machine learning, but it is not yet clear whether it will actually be valuable for practical problems. While some artificial scenarios have shown that certain quantum machine learning techniques may be advantageous co...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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American Physical Society
2025-07-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/c43x-9866 |
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| _version_ | 1849701996346474496 |
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| author | Akimoto Nakayama Kosuke Mitarai Leonardo Placidi Takanori Sugimoto Keisuke Fujii |
| author_facet | Akimoto Nakayama Kosuke Mitarai Leonardo Placidi Takanori Sugimoto Keisuke Fujii |
| author_sort | Akimoto Nakayama |
| collection | DOAJ |
| description | Quantum machine learning has the potential to computationally outperform classical machine learning, but it is not yet clear whether it will actually be valuable for practical problems. While some artificial scenarios have shown that certain quantum machine learning techniques may be advantageous compared to their classical counterpart, evidence does not yet suggest that quantum machine learning has surpassed conventional approaches in dealing with standard classical datasets, such as the MNIST dataset. In contrast, dealing with quantum data, such as quantum states or circuits, may be the task where we can benefit from quantum methods. Therefore, it is important to develop practically meaningful quantum datasets for which we expect quantum methods to be superior. In this paper, we propose a machine learning task that is likely to soon arise in the real world: clustering and classification of quantum circuits. We provide a dataset of quantum circuits optimized by the variational quantum eigensolver. We utilized six common types of Hamiltonians in condensed matter physics, with a range of 4–20 qubits, and applied ten different ansatz with varying depths (ranging from 3 to 32) to generate a quantum circuit dataset of six distinct classes, each containing 300 samples. We show that this dataset can be easily learned using quantum methods. In particular, we demonstrate a successful classification of our dataset using real four-qubit devices available through IBMQ. By providing a setting and an elementary dataset where quantum machine learning is expected to be beneficial, we hope to encourage and ease the advancement of the field. |
| format | Article |
| id | doaj-art-966888d30a994a738b9d4dc3452bcd16 |
| institution | DOAJ |
| issn | 2643-1564 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj-art-966888d30a994a738b9d4dc3452bcd162025-08-20T03:17:47ZengAmerican Physical SocietyPhysical Review Research2643-15642025-07-017303304810.1103/c43x-9866VQE-generated quantum circuit dataset for machine learningAkimoto NakayamaKosuke MitaraiLeonardo PlacidiTakanori SugimotoKeisuke FujiiQuantum machine learning has the potential to computationally outperform classical machine learning, but it is not yet clear whether it will actually be valuable for practical problems. While some artificial scenarios have shown that certain quantum machine learning techniques may be advantageous compared to their classical counterpart, evidence does not yet suggest that quantum machine learning has surpassed conventional approaches in dealing with standard classical datasets, such as the MNIST dataset. In contrast, dealing with quantum data, such as quantum states or circuits, may be the task where we can benefit from quantum methods. Therefore, it is important to develop practically meaningful quantum datasets for which we expect quantum methods to be superior. In this paper, we propose a machine learning task that is likely to soon arise in the real world: clustering and classification of quantum circuits. We provide a dataset of quantum circuits optimized by the variational quantum eigensolver. We utilized six common types of Hamiltonians in condensed matter physics, with a range of 4–20 qubits, and applied ten different ansatz with varying depths (ranging from 3 to 32) to generate a quantum circuit dataset of six distinct classes, each containing 300 samples. We show that this dataset can be easily learned using quantum methods. In particular, we demonstrate a successful classification of our dataset using real four-qubit devices available through IBMQ. By providing a setting and an elementary dataset where quantum machine learning is expected to be beneficial, we hope to encourage and ease the advancement of the field.http://doi.org/10.1103/c43x-9866 |
| spellingShingle | Akimoto Nakayama Kosuke Mitarai Leonardo Placidi Takanori Sugimoto Keisuke Fujii VQE-generated quantum circuit dataset for machine learning Physical Review Research |
| title | VQE-generated quantum circuit dataset for machine learning |
| title_full | VQE-generated quantum circuit dataset for machine learning |
| title_fullStr | VQE-generated quantum circuit dataset for machine learning |
| title_full_unstemmed | VQE-generated quantum circuit dataset for machine learning |
| title_short | VQE-generated quantum circuit dataset for machine learning |
| title_sort | vqe generated quantum circuit dataset for machine learning |
| url | http://doi.org/10.1103/c43x-9866 |
| work_keys_str_mv | AT akimotonakayama vqegeneratedquantumcircuitdatasetformachinelearning AT kosukemitarai vqegeneratedquantumcircuitdatasetformachinelearning AT leonardoplacidi vqegeneratedquantumcircuitdatasetformachinelearning AT takanorisugimoto vqegeneratedquantumcircuitdatasetformachinelearning AT keisukefujii vqegeneratedquantumcircuitdatasetformachinelearning |