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Meet Gandhi

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2 papers

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AAMAS Conference 2026 Conference Paper

Distributed Quantum Gaussian Processes for Multi-Agent Systems

Meet Gandhi
George P. Kontoudis

Gaussian Processes (GPs) are a powerful tool for probabilistic modeling, but their performance is often constrained in complex, largescale 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 multiagent 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.

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AAMAS Conference 2026 Conference Paper

Quantum-Enhanced Learning and Control for Multi-agent Systems

Meet Gandhi

With multi-agent systems advancing to high-dimensional and uncertain spaces, classical learning and control methods encounter fundamental challenges related to the curse of dimensionality and limited expressivity. Quantum computing offers the potential to overcome the shortcomings by embedding data into exponentially large Hilbert spaces, capturing complex correlations. We first propose a Distributed Quantum Gaussian Process (DQGP) framework enabling agents to collaboratively learn a high-fidelity global model of the environment through improved modeling capabilities and scalability. Numerical evaluations on non-stationary NASA SRTM datasets demonstrate the enhanced predictive and uncertainty estimation performance of DQGP compared to the classical Distributed GaussianProcesses. Thefindingsleadtothenextresearchphase: developing a Quantum-enhanced Learning Model Predictive Control architecture that results in robust, adaptive, and scalable coordination and control of multiple agents in complex scenarios.

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