Synergic quantum generative machine learning
Abstract We introduce a new approach towards generative quantum machine learning significantly reducing the number of hyperparameters and report on a proof-of-principle experiment demonstrating our approach. Our proposal depends on collaboration between the generators and discriminator, thus, we cal...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-40137-1 |
| _version_ | 1797453137695473664 |
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| author | Karol Bartkiewicz Patrycja Tulewicz Jan Roik Karel Lemr |
| author_facet | Karol Bartkiewicz Patrycja Tulewicz Jan Roik Karel Lemr |
| author_sort | Karol Bartkiewicz |
| collection | DOAJ |
| description | Abstract We introduce a new approach towards generative quantum machine learning significantly reducing the number of hyperparameters and report on a proof-of-principle experiment demonstrating our approach. Our proposal depends on collaboration between the generators and discriminator, thus, we call it quantum synergic generative learning. We present numerical evidence that the synergic approach, in some cases, compares favorably to recently proposed quantum generative adversarial learning. In addition to the results obtained with quantum simulators, we also present experimental results obtained with an actual programmable quantum computer. We investigate how a quantum computer implementing generative learning algorithm could learn the concept of a maximally-entangled state. After completing the learning process, the network is able both to recognize and to generate an entangled state. Our approach can be treated as one possible preliminary step to understanding how the concept of quantum entanglement can be learned and demonstrated by a quantum computer. |
| first_indexed | 2024-03-09T15:18:35Z |
| format | Article |
| id | doaj.art-79623d682b82444c8ca4b55d48a3a2db |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:18:35Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-79623d682b82444c8ca4b55d48a3a2db2023-11-26T12:57:15ZengNature PortfolioScientific Reports2045-23222023-08-0113111410.1038/s41598-023-40137-1Synergic quantum generative machine learningKarol Bartkiewicz0Patrycja Tulewicz1Jan Roik2Karel Lemr3Institute of Spintronics and Quantum Information, Adam Mickiewicz UniversityInstitute of Spintronics and Quantum Information, Adam Mickiewicz UniversityJoint Laboratory of Optics of Palacký University and Institute of Physics of Czech Academy of SciencesJoint Laboratory of Optics of Palacký University and Institute of Physics of Czech Academy of SciencesAbstract We introduce a new approach towards generative quantum machine learning significantly reducing the number of hyperparameters and report on a proof-of-principle experiment demonstrating our approach. Our proposal depends on collaboration between the generators and discriminator, thus, we call it quantum synergic generative learning. We present numerical evidence that the synergic approach, in some cases, compares favorably to recently proposed quantum generative adversarial learning. In addition to the results obtained with quantum simulators, we also present experimental results obtained with an actual programmable quantum computer. We investigate how a quantum computer implementing generative learning algorithm could learn the concept of a maximally-entangled state. After completing the learning process, the network is able both to recognize and to generate an entangled state. Our approach can be treated as one possible preliminary step to understanding how the concept of quantum entanglement can be learned and demonstrated by a quantum computer.https://doi.org/10.1038/s41598-023-40137-1 |
| spellingShingle | Karol Bartkiewicz Patrycja Tulewicz Jan Roik Karel Lemr Synergic quantum generative machine learning Scientific Reports |
| title | Synergic quantum generative machine learning |
| title_full | Synergic quantum generative machine learning |
| title_fullStr | Synergic quantum generative machine learning |
| title_full_unstemmed | Synergic quantum generative machine learning |
| title_short | Synergic quantum generative machine learning |
| title_sort | synergic quantum generative machine learning |
| url | https://doi.org/10.1038/s41598-023-40137-1 |
| work_keys_str_mv | AT karolbartkiewicz synergicquantumgenerativemachinelearning AT patrycjatulewicz synergicquantumgenerativemachinelearning AT janroik synergicquantumgenerativemachinelearning AT karellemr synergicquantumgenerativemachinelearning |