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.
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.
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 Gaussian Processes. The findings lead to the next research phase: 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.
The utilization of Quantum Multi-Agent Systems (MAS) and Quantum Protocols in distributed agent environments has gained attention due to the need for enhanced protocol efficiency in quantum computing applications. Conventional methods often face limitations in achieving optimal performance, hindering the full potential of quantum computing in distributed settings. Existing approaches lack the necessary robustness to fully exploit the advantages offered by Quantum MAS, leading to inefficiencies in computational performance within distributed agent environments. In this context, we propose a novel Quantum MAS framework, which harnesses the principles of quantum superposition, entanglement, and advanced Quantum Protocols, including the quantum key distribution mechanism. The framework facilitates collaborative decision-making among agents through the utilization of joint quantum states and enables seamless synchronization of actions via the entanglement operator. The computational efficiency is optimized using quantum gate operations, thereby enhancing the overall computational performance in the distributed agent environment. We quantify the efficiency, showcasing the significant improvements achieved by the proposed Quantum MAS framework. Our research employs diverse datasets, including synthetic and real-world data, to comprehensively evaluate the performance and efficacy of the proposed Quantum MAS framework. Experimental results demonstrate a notable efficiency enhancement, with the proposed Quantum MAS achieving an average efficiency value of 0.92 across various experimental configurations and datasets. The findings underscore the significant potential of Quantum MAS in effectively addressing efficiency concerns within distributed agent environments, thus paving the way for broader applications of quantum computing in real-world scenarios.
Gaussian process regression (GPR) is a nonparametric supervised learning method, widely valued for its ability to quantify uncertainty. Despite its advantages and broad applications, classical GPR implementations face significant scalability challenges, as they involve matrix operations with a cubic complexity in relation to the dataset size. This computational challenge is further compounded by the demand of optimizing the Gaussian Process (GP) model over its hyperparameters, rendering the total computational cost prohibitive for data intensive problems. To address this issue, we propose a quantum gradient descent algorithm to optimize the GP model. Taking advantage of recent advances in quantum algorithms for linear algebra, our algorithm achieves exponential speedup in computing the gradients of the log marginal likelihood. The entire gradient descent process is integrated into the quantum circuit. Through runtime analysis and error bounds, we demonstrate that our approach significantly improves the scalability of GPR model optimization, making it computationally feasible for large-scale applications.
In this paper, we introduce Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning (Dist-QTRL), a novel approach to addressing the scalability challenges of traditional Reinforcement Learning (RL) by integrating quantum computing principles. Quantum-Train Reinforcement Learning (QTRL) leverages parameterized quantum circuits to efficiently generate neural network parameters, achieving a poly(log(N))reduction in the dimensionality of trainable parameters while harnessing quantum entanglement for superior data representation. The framework is designed for distributed multi-agent environments, where multiple agents, modeled as Quantum Processing Units (QPUs), operate in parallel, enabling faster convergence and enhanced scalability. Additionally, the Dist-QTRL framework can be extended to high-performance computing (HPC) environments by utilizing distributed quantum training for parameter reduction in classical neural networks, followed by inference using classical CPUs or GPUs. This hybrid quantumHPC approach allows for further optimization in real-world applications. In this paper, we provide a mathematical formulation of the Dist-QTRL framework and explore its convergence properties, supported by empirical results demonstrating performance improvements over centric QTRL models. The results highlight the potential of quantum-enhanced RL in tackling complex, highdimensional tasks, particularly in distributed computing settings, where our framework achieves significant speedups through parallelization without compromising model accuracy. This work paves the way for scalable, quantum-enhanced RL systems in practical applications, leveraging both quantum and classical computational resources.
This paper explores a novel approach to distributed optimization leveraging the unique properties of quantum entanglement. Traditional distributed optimization methods often suffer from communication bottlenecks and reliance on shared information, limiting scalability and efficiency, particularly in scenarios with geographically dispersed agents. This research proposes a framework where agents collaborate to solve complex optimization problems through the utilization of quantum entanglement, effectively creating a "quantum entanglement network". The core idea is to exploit the non-local correlations inherent in entangled states for instantaneous information exchange, bypassing the need for direct communication channels. We detail a communication protocol based on entangled state manipulation and describe how an optimization algorithm can utilize this network to guide local decisions toward a global optimum. The theoretical framework demonstrates the potential for enhanced scalability and efficiency in distributed optimization, representing a significant departure from conventional approaches. The proposed method addresses the inherent limitations of classical communication in complex distributed systems, offering a fundamentally different paradigm for collaborative problem-solving. ---
Optimization problems are widely encountered in real-world domains such as energy systems, financial markets, transportation networks, supply chain logistics, manufacturing processes, and e-commerce platforms. Traditional classical computing methods often struggle to solve large combinatorial optimization problems due to the exponential growth of the solution space. Quantum computing has emerged as a promising approach to address these challenges by leveraging quantum mechanical principles such as superposition and entanglement. However, current quantum hardware is limited by noise, restricted qubit availability, and scalability issues, making fully quantum solutions impractical for large-scale applications. To overcome these limitations, this research proposes a hybrid quantum–classical supercomputing framework for solving complex optimization problems across multiple domains. The framework integrates classical data processing and optimization techniques with quantum-inspired algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA), to efficiently explore large solution spaces. Real-world datasets from energy systems, supply chain logistics, financial markets, transportation networks, manufacturing processes, and e-commerce platforms are used for experimental evaluation. The results demonstrate that the hybrid optimization approach can generate higher-quality solutions and improved optimization accuracy compared to traditional classical methods. These findings highlight the potential of hybrid quantum–classical systems as an effective and scalable solution for solving complex optimization problems in modern computational applications.