A general learning scheme for classical and quantum Ising machines
An Ising machine is any hardware specifically designed for finding the ground state of the Ising model. Relevant examples are coherent Ising machines and quantum annealers. In this paper, we propose a new machine learning model that is based on the Ising structure and can be efficiently trained usin...
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| Format: | Article |
| Language: | English |
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SciPost
2024-03-01
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| Series: | SciPost Physics Core |
| Online Access: | https://scipost.org/SciPostPhysCore.7.1.013 |
| _version_ | 1797262244532191232 |
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| author | Ludwig Schmid, Enrico Zardini, Davide Pastorello |
| author_facet | Ludwig Schmid, Enrico Zardini, Davide Pastorello |
| author_sort | Ludwig Schmid, Enrico Zardini, Davide Pastorello |
| collection | DOAJ |
| description | An Ising machine is any hardware specifically designed for finding the ground state of the Ising model. Relevant examples are coherent Ising machines and quantum annealers. In this paper, we propose a new machine learning model that is based on the Ising structure and can be efficiently trained using gradient descent. We provide a mathematical characterization of the training process, which is based upon optimizing a loss function whose partial derivatives are not explicitly calculated but estimated by the Ising machine itself. Moreover, we present some experimental results on the training and execution of the proposed learning model. These results point out new possibilities offered by Ising machines for different learning tasks. In particular, in the quantum realm, the quantum resources are used for both the execution and the training of the model, providing a promising perspective in quantum machine learning. |
| first_indexed | 2024-04-24T23:54:02Z |
| format | Article |
| id | doaj.art-7467b7a7a909402aaff7f4ceb84ab378 |
| institution | Directory Open Access Journal |
| issn | 2666-9366 |
| language | English |
| last_indexed | 2024-04-24T23:54:02Z |
| publishDate | 2024-03-01 |
| publisher | SciPost |
| record_format | Article |
| series | SciPost Physics Core |
| spelling | doaj.art-7467b7a7a909402aaff7f4ceb84ab3782024-03-14T15:35:26ZengSciPostSciPost Physics Core2666-93662024-03-017101310.21468/SciPostPhysCore.7.1.013A general learning scheme for classical and quantum Ising machinesLudwig Schmid, Enrico Zardini, Davide PastorelloAn Ising machine is any hardware specifically designed for finding the ground state of the Ising model. Relevant examples are coherent Ising machines and quantum annealers. In this paper, we propose a new machine learning model that is based on the Ising structure and can be efficiently trained using gradient descent. We provide a mathematical characterization of the training process, which is based upon optimizing a loss function whose partial derivatives are not explicitly calculated but estimated by the Ising machine itself. Moreover, we present some experimental results on the training and execution of the proposed learning model. These results point out new possibilities offered by Ising machines for different learning tasks. In particular, in the quantum realm, the quantum resources are used for both the execution and the training of the model, providing a promising perspective in quantum machine learning.https://scipost.org/SciPostPhysCore.7.1.013 |
| spellingShingle | Ludwig Schmid, Enrico Zardini, Davide Pastorello A general learning scheme for classical and quantum Ising machines SciPost Physics Core |
| title | A general learning scheme for classical and quantum Ising machines |
| title_full | A general learning scheme for classical and quantum Ising machines |
| title_fullStr | A general learning scheme for classical and quantum Ising machines |
| title_full_unstemmed | A general learning scheme for classical and quantum Ising machines |
| title_short | A general learning scheme for classical and quantum Ising machines |
| title_sort | general learning scheme for classical and quantum ising machines |
| url | https://scipost.org/SciPostPhysCore.7.1.013 |
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