Non-native Quantum Generative Optimization with Adversarial Autoencoders

TL;DR

This paper introduces the adversarial quantum autoencoder model (AQAM) that enables large-scale optimization problems to be efficiently mapped onto existing quantum samplers, improving their performance through quantum-enhanced sampling techniques.

Contribution

The paper presents a novel AQAM approach that addresses encoding limitations of quantum samplers and optimizes problems via latent quantum-enhanced Boltzmann sampling, demonstrated on neutral atom hardware.

Findings

AQAM achieves lower Renyi divergence than classical methods

Demonstrated optimization of 64x64 unit cell metasurfaces

Improved spectral gap in quantum sampling compared to classical algorithms

Abstract

Large-scale optimization problems are prevalent in several fields, including engineering, finance, and logistics. However, most optimization problems cannot be efficiently encoded onto a physical system because the existing quantum samplers have too few qubits. Another typical limiting factor is that the optimization constraints are not compatible with the native cost Hamiltonian. This work presents a new approach to address these challenges. We introduce the adversarial quantum autoencoder model (AQAM) that can be used to map large-scale optimization problems onto existing quantum samplers while simultaneously optimizing the problem through latent quantum-enhanced Boltzmann sampling. We demonstrate the AQAM on a neutral atom sampler, and showcase the model by optimizing 64px by 64px unit cells that represent a broad-angle filter metasurface applicable to improving the coherence of neutral atom devices. Using 12-atom simulations, we demonstrate that the AQAM achieves a lower Renyi divergence and a larger spectral gap when compared to classical Markov Chain Monte Carlo samplers. Our work paves the way to more efficient mapping of conventional optimization problems into existing quantum samplers.