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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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2019-10-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.1.033063 |
| _version_ | 1797211666335662080 |
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| author | Nathan Killoran Thomas R. Bromley Juan Miguel Arrazola Maria Schuld Nicolás Quesada Seth Lloyd |
| author_facet | Nathan Killoran Thomas R. Bromley Juan Miguel Arrazola Maria Schuld Nicolás Quesada Seth Lloyd |
| author_sort | Nathan Killoran |
| collection | DOAJ |
| 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 | 2024-04-24T10:30:07Z |
| format | Article |
| id | doaj.art-55798a4c9bd74bd0a62e3f02ddd1cf35 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:30:07Z |
| publishDate | 2019-10-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-55798a4c9bd74bd0a62e3f02ddd1cf352024-04-12T16:46:32ZengAmerican Physical SocietyPhysical Review Research2643-15642019-10-011303306310.1103/PhysRevResearch.1.033063Continuous-variable quantum neural networksNathan KilloranThomas R. BromleyJuan Miguel ArrazolaMaria SchuldNicolás QuesadaSeth LloydWe 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.http://doi.org/10.1103/PhysRevResearch.1.033063 |
| spellingShingle | Nathan Killoran Thomas R. Bromley Juan Miguel Arrazola Maria Schuld Nicolás Quesada Seth Lloyd Continuous-variable quantum neural networks Physical Review Research |
| 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 | http://doi.org/10.1103/PhysRevResearch.1.033063 |
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