Distributed Quantum Gaussian Processes for Multi-Agent Systems

TL;DR

This paper introduces a Distributed Quantum Gaussian Process framework for multi-agent systems, leveraging quantum computing to improve modeling capacity and scalability, with a novel optimization algorithm and experimental validation on simulators.

Contribution

It presents a new Distributed Quantum Gaussian Process method with a consensus Riemannian ADMM algorithm, demonstrating enhanced modeling and potential speedups in quantum hardware.

Findings

Effective modeling of elevation data using quantum GPs.

Scalability achieved through distributed consensus optimization.

Potential computational speedups with quantum hardware.

Abstract

Gaussian Processes (GPs) are a powerful tool for probabilistic modeling, but their performance is often constrained in complex, large-scale real-world domains due to the limited expressivity of classical kernels. Quantum computing offers the potential to overcome this limitation by embedding data into exponentially large Hilbert spaces, capturing complex correlations that remain inaccessible to classical computing approaches. In this paper, we propose a Distributed Quantum Gaussian Process (DQGP) method in a multi-agent setting to enhance modeling capabilities and scalability. To address the challenging non-Euclidean optimization problem, we develop a Distributed consensus Riemannian Alternating Direction Method of Multipliers (DR-ADMM) algorithm that aggregates local agent models into a global model. We evaluate the efficacy of our method through numerical experiments conducted on a quantum simulator in classical hardware. We use real-world, non-stationary elevation datasets of NASA's Shuttle Radar Topography Mission and synthetic datasets generated by Quantum Gaussian Processes. Beyond modeling advantages, our framework highlights potential computational speedups that quantum hardware may provide, particularly in Gaussian processes and distributed optimization.