Learning and Inference on Generative Adversarial Quantum Circuits

TL;DR

This paper introduces a hybrid quantum-classical adversarial training method for quantum generative models, demonstrating potential quantum advantages in data inference and generation on near-term quantum devices.

Contribution

It proposes a novel adversarial training scheme for quantum circuits as generative models, enabling efficient data inference and simulation on quantum hardware.

Findings

Successful numerical simulation on Bars-and-Stripes dataset

Quantum inference shows quadratic speed-up via amplitude amplification

Hybrid training scheme effectively learns quantum generative models

Abstract

Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of quantum circuits can be more challenging compared to classical neural networks due to lack of efficient differentiable learning algorithm. We devise an adversarial quantum-classical hybrid training scheme via coupling a quantum circuit generator and a classical neural network discriminator together. After training, the quantum circuit generative model can infer missing data with quadratic speed up via amplitude amplification. We numerically simulate the learning and inference of generative adversarial quantum circuit using the prototypical Bars-and-Stripes dataset. Generative adversarial quantum circuits is a fresh approach to machine learning which may enjoy the practically useful quantum advantage on near-term quantum devices.