Quantum Wasserstein GANs for State Preparation at Unseen Points of a Phase Diagram

TL;DR

This paper introduces a hybrid quantum-classical Wasserstein GAN that can generate new quantum states at unseen points in a phase diagram by learning the underlying measurement expectation functions, surpassing previous models limited to training data.

Contribution

A novel hybrid quantum-classical Wasserstein GAN framework capable of generating unseen quantum states based on learned measurement expectation functions.

Findings

Successfully generates states at unseen phase diagram points.

Learns underlying measurement expectation functions.

Overcomes limitations of previous quantum GANs.

Abstract

Generative models and in particular Generative Adversarial Networks (GANs) have become very popular and powerful data generation tool. In recent years, major progress has been made in extending this concept into the quantum realm. However, most of the current methods focus on generating classes of states that were supplied in the input set and seen at the training time. In this work, we propose a new hybrid classical-quantum method based on quantum Wasserstein GANs that overcomes this limitation. It allows to learn the function governing the measurement expectations of the supplied states and generate new states, that were not a part of the input set, but which expectations follow the same underlying function.