Variational Quantum Generators: Generative Adversarial Quantum Machine Learning for Continuous Distributions. Issue 1 (3rd December 2020)

• Record Type: Journal Article
• Authors: Romero, Jonathan; Aspuru‐Guzik, Alán
• Is Part Of: Advanced quantum technologies. Volume 4:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-03
• Subjects: generative adversarial network -- generative modeling -- machine learning -- quantum computing -- variational quantum algorithms; Quantum theory -- Periodicals; Quantum computing -- Periodicals; Quantum chemistry -- Periodicals; Quantum electronics -- Periodicals; 537.5
• DOI: 10.1002/qute.202000003
• Languages: English
• ISSNs: 2511-9044

Abstract

Abstract: A hybrid quantum–classical approach to model continuous classical probability distributions using a variational quantum circuit is proposed. The architecture of this quantum generator consists of a quantum circuit that encodes a classical random variable into a quantum state and a parameterized quantum circuit trained to mimic the target distribution. The model allows for easy interfacing with a classical function, such as a neural network, and is trained using an adversarial learning approach. It is shown that the quantum generator is able to learn using either a classical neural network or a variational quantum circuit as the discriminator model. This implementation takes advantage of automatic differentiation tools to perform the optimization of the variational circuits employed. The framework presented here for the design and implementation of the variational quantum generators can serve as a blueprint for designing hybrid quantum–classical models for other machine learning tasks. Abstract : Continuous classical probability distributions are modeled using hybrid quantum–classical generative adversarial networks, where both generator and discriminator consist of a quantum encoder, that maps classical information to quantum states, and a variational circuit. Classical distributions are obtained by sampling the generator and measuring observables on the states generated. This framework provides a blueprint for designing hybrid quantum–classical machine learning architectures.