Quantum Wasserstein GANs for State Preparation at Unseen Points of a Phase Diagram
This work addresses the problem of generating quantum states at unseen points in phase diagrams for researchers in quantum computing and physics, representing an incremental advance over prior methods.
The authors tackled the limitation of existing quantum generative models that only generate states seen during training, by proposing a hybrid classical-quantum method based on quantum Wasserstein GANs to learn the function governing measurement expectations and generate new, unseen states with the same underlying function.
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.