Continuous-variable quantum neural networks
We introduce a general method for building neural networks on quantum computers. The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromag...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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American Physical Society (APS)
2020
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| Online Access: | https://hdl.handle.net/1721.1/126828 |
| _version_ | 1826198481345183744 |
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| author | Killoran, Nathan Bromley, Thomas R. Arrazola, Juan Miguel Schuld, Maria Quesada, Nicolás Lloyd, Seth |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Killoran, Nathan Bromley, Thomas R. Arrazola, Juan Miguel Schuld, Maria Quesada, Nicolás Lloyd, Seth |
| author_sort | Killoran, Nathan |
| collection | MIT |
| description | We introduce a general method for building neural networks on quantum computers. The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field. This circuit contains a layered structure of continuously parameterized gates which is universal for CV quantum computation. Affine transformations and nonlinear activation functions, two key elements in neural networks, are enacted in the quantum network using Gaussian and non-Gaussian gates, respectively. The non-Gaussian gates provide both the nonlinearity and the universality of the model. Due to the structure of the CV model, the CV quantum neural network can encode highly nonlinear transformations while remaining completely unitary. We show how a classical network can be embedded into the quantum formalism and propose quantum versions of various specialized models such as convolutional, recurrent, and residual networks. Finally, we present numerous modeling experiments built with the strawberry fields software library. These experiments, including a classifier for fraud detection, a network which generates tetris images, and a hybrid classical-quantum autoencoder, demonstrate the capability and adaptability of CV quantum neural networks. |
| first_indexed | 2024-09-23T11:05:38Z |
| format | Article |
| id | mit-1721.1/126828 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:05:38Z |
| publishDate | 2020 |
| publisher | American Physical Society (APS) |
| record_format | dspace |
| spelling | mit-1721.1/1268282022-09-27T17:05:53Z Continuous-variable quantum neural networks Killoran, Nathan Bromley, Thomas R. Arrazola, Juan Miguel Schuld, Maria Quesada, Nicolás Lloyd, Seth Massachusetts Institute of Technology. Department of Mechanical Engineering We introduce a general method for building neural networks on quantum computers. The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field. This circuit contains a layered structure of continuously parameterized gates which is universal for CV quantum computation. Affine transformations and nonlinear activation functions, two key elements in neural networks, are enacted in the quantum network using Gaussian and non-Gaussian gates, respectively. The non-Gaussian gates provide both the nonlinearity and the universality of the model. Due to the structure of the CV model, the CV quantum neural network can encode highly nonlinear transformations while remaining completely unitary. We show how a classical network can be embedded into the quantum formalism and propose quantum versions of various specialized models such as convolutional, recurrent, and residual networks. Finally, we present numerous modeling experiments built with the strawberry fields software library. These experiments, including a classifier for fraud detection, a network which generates tetris images, and a hybrid classical-quantum autoencoder, demonstrate the capability and adaptability of CV quantum neural networks. 2020-08-27T17:49:03Z 2020-08-27T17:49:03Z 2019-10 2018-08 2020-07-30T17:04:54Z Article http://purl.org/eprint/type/JournalArticle 2643-1564 https://hdl.handle.net/1721.1/126828 Killoran, Nathan et al. "Continuous-variable quantum neural networks." Physical Review Research 1, 3 (October 2019): 033063 en http://dx.doi.org/10.1103/physrevresearch.1.033063 Physical Review Research Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf American Physical Society (APS) APS |
| spellingShingle | Killoran, Nathan Bromley, Thomas R. Arrazola, Juan Miguel Schuld, Maria Quesada, Nicolás Lloyd, Seth Continuous-variable quantum neural networks |
| title | Continuous-variable quantum neural networks |
| title_full | Continuous-variable quantum neural networks |
| title_fullStr | Continuous-variable quantum neural networks |
| title_full_unstemmed | Continuous-variable quantum neural networks |
| title_short | Continuous-variable quantum neural networks |
| title_sort | continuous variable quantum neural networks |
| url | https://hdl.handle.net/1721.1/126828 |
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