Continuous-variable quantum neural networks
Name
PhysRevResearch.1.033063.pdf
Description
Published version
Size
1.37 MB
Format
Adobe PDF
Checksum (MD5)
b954cb45ee67a73ea17ef0d5794db4f6
Author(s)
Killoran, Nathan
Bromley, Thomas R.
Arrazola, Juan Miguel
Schuld, Maria
Quesada, Nicolás
Lloyd, Seth
Date Issued
October 2019
Journal
Physical Review Research
Publisher
American Physical Society (APS)
Citation
Killoran, Nathan et al. "Continuous-variable quantum neural networks." Physical Review Research 1, 3 (October 2019): 033063
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1103/physrevresearch.1.033063
