Continuous-variable quantum neural networks
Oct 31, 2019
22 pages
Published in:
- Phys.Rev.Res. 1 (2019) 3, 033063
- Published: Oct 31, 2019
Citations per year
Abstract: (APS)
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.- Quantum Information, Science & Technology
- Machine learning
- Optical quantum information processing
- Quantum algorithms
- Quantum gates
- Quantum information processing with continuous variables
- neural network: quantum
- gate
- quantum information
- machine learning
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