Continuous-variable quantum neural networks

TL;DR

This paper introduces a universal continuous-variable quantum neural network framework using variational quantum circuits with Gaussian and non-Gaussian gates, enabling complex nonlinear transformations for quantum machine learning tasks.

Contribution

It presents a novel CV quantum neural network architecture with layered parameterized gates, embedding classical neural network models into quantum formalism and demonstrating practical applications.

Findings

Successfully implemented quantum classifiers and generative models.

Demonstrated the network's ability to encode nonlinear transformations.

Showcased adaptability through various modeling experiments.

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 model 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.