Random Quantum Circuits as Seeds for Continuous Generative Models

TL;DR

This paper proposes using random quantum circuits as seeds for classical generative models, demonstrating their robustness against simulation and their potential for hybrid quantum-classical systems on NISQ devices.

Contribution

It introduces a new class of random circuits that serve as effective seeds for hybrid models, highlighting their robustness and diversity in data generation.

Findings

Circuits are robust against tensor network and Pauli propagation simulations.

Local variables do not concentrate, ensuring data diversity.

Suitable for NISQ-era quantum-classical hybrid models.

Abstract

We introduce a random circuit family and show they are robust against current classical simulation techniques, specifically tensor network contraction and Pauli propagation. We also show that local variables do not concentrate, ensuring enough variance to be able to produce a diverse set of data points. We therefore argue that using these circuits as a "random seed" for a larger classical generative model is a way to make large-scale quantum-classical hybrid models amenable towards NISQ devices.