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[2602.15006] Distributed Quantum Gaussian Processes for Multi-Agent Systems
Summary
This article presents a novel Distributed Quantum Gaussian Process (DQGP) method for multi-agent systems, enhancing modeling capabilities through quantum computing and addressing optimization challenges.
Why It Matters
The integration of quantum computing with Gaussian processes could significantly improve the scalability and performance of machine learning models in complex environments. This research is particularly relevant as it explores the intersection of quantum technology and multi-agent systems, potentially paving the way for advancements in AI applications.
Key Takeaways
- Quantum computing can enhance the expressivity of Gaussian processes.
- The proposed DQGP method addresses optimization challenges in multi-agent settings.
- Numerical experiments demonstrate the efficacy of the method using real-world datasets.
- The framework may offer computational speedups with quantum hardware.
- This research contributes to the evolving field of quantum machine learning.
Computer Science > Multiagent Systems arXiv:2602.15006 (cs) [Submitted on 16 Feb 2026] Title:Distributed Quantum Gaussian Processes for Multi-Agent Systems Authors:Meet Gandhi, George P. Kontoudis View a PDF of the paper titled Distributed Quantum Gaussian Processes for Multi-Agent Systems, by Meet Gandhi and 1 other authors View PDF HTML (experimental) Abstract: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 fram...
