Optimized control of quantum networks is essential for enabling distributed quantum applications with strict performance requirements. In near-term architectures with constrained hardware, effective control may determine the feasibility of deploying such applications. Because quantum network dynamics are suitable for being modeled as a Markov decision process, dynamic programming and reinforcement learning (RL) offer promising tools for optimizing control strategies. However, key quantum network performance measures -- such as secret key rate in quantum key distribution -- often involve a non-linear relationship between interdependent variables that describe quantum state quality and generation rate. Such objectives are not easily captured by standard RL approaches based on additive rewards. We propose a novel gradient-based RL framework that directly optimizes non-linear, differentiable objective functions, while accounting for uncertainties introduced by classical communication delays. We evaluate this framework in the context of entanglement distillation between two quantum network nodes equipped with multiplexing capability, and demonstrate up to 20-23% improvement over heuristic baselines in certain parameter regimes. Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
Optimized control of quantum networks is essential for enabling distributed quantum applications with strict performance requirements. In near-term architectures with constrained hardware, effective control may determine the feasibility of deploying such applications. Because quantum network dynamics are suitable for being modeled as a Markov decision process, dynamic programming and reinforcement learning (RL) offer promising tools for optimizing control strategies. However, key quantum network performance measures -- such as secret key rate in quantum key distribution -- often involve a non-linear relationship between interdependent variables that describe quantum state quality and generation rate. Such objectives are not easily captured by standard RL approaches based on additive rewards. We propose a novel gradient-based RL framework that directly optimizes non-linear, differentiable objective functions, while accounting for uncertainties introduced by classical communication delays. We evaluate this framework in the context of entanglement distillation between two quantum network nodes equipped with multiplexing capability, and demonstrate up to 20-23% improvement over heuristic baselines in certain parameter regimes. Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
Quantum networks make use of the quantum entanglement as building block. When two qubits are entangled, their state changes exhibit non-classical correlations used to design new applications not possible with classical communication, such us quantum key distribution or distributed quantum computation. Unfortunately, quantum entanglement is a probabilistic process strongly dependent on the features of involved devices (optical fibers, lasers, quantum memories, …). The management decisions (i.e., the control policy) to set up and keep the entanglement as long as possible with the highest quality constitutes a stochastic control problem. This process can be modelled as Markov Decision Process (MDP) and solved via the Reinforcement Learning (RL) framework (a form of Machine Learning). In this work, we apply this RL framework to learn an entanglement management policy outperforming the State-of-the-Art policy when models characterising precisely the involved quantum devices are not known.
This paper presents a novel algorithm for optimizing the allocation of entangled resources within quantum networks. The core of the approach lies in employing a reinforcement learning (RL) framework to dynamically manage and distribute entanglement, addressing the inherent challenges of maximizing utilization and enhancing quantum communication efficiency. Unlike traditional static allocation methods, the proposed RL agent learns optimal allocation strategies through iterative interaction with a simulated quantum network environment. The algorithm considers factors such as node connectivity, qubit availability, and communication delays to minimize resource wastage and improve overall network performance. The key performance indicator (KPI) is the entanglement fidelity between communicating nodes. The developed algorithm offers a promising pathway towards realizing the full potential of quantum networks by intelligently managing their most valuable asset: entanglement. The primary goal is to achieve higher fidelity entanglement distribution and improved communication rates. The effectiveness of the algorithm is evaluated through simulations, demonstrating its ability to outperform existing allocation schemes in various network topologies and operational scenarios.
This paper addresses the critical challenge of optimizing resource allocation within dynamic quantum networks (QNs). The inherent complexities of QNs, including fluctuating demands, evolving topologies, and the fragility of quantum states, necessitate adaptive resource management strategies. We propose a novel algorithm leveraging reinforcement learning (RL) to dynamically adjust resource allocation based on real-time network conditions. The algorithm learns an optimal policy by iteratively interacting with a simulated QN environment, maximizing the overall quantum channel throughput while minimizing the probability of quantum error. Specifically, the core mechanism involves formulating the resource allocation problem as a Markov Decision Process (MDP), where the state represents the network's current configuration, the action represents the allocation of quantum resources (e.g., qubits, entanglement), and the reward reflects the successful completion of quantum communication tasks. The RL agent utilizes a Q-learning algorithm to estimate the optimal Q-function, guiding the allocation process. We demonstrate, through simulation, that our algorithm significantly outperforms static allocation strategies in terms of throughput and reliability, even under highly dynamic network conditions. The key contributions of this work lie in the integration of RL with the unique characteristics of QNs and the development of a practical algorithm for adaptive resource management. Further research will explore the scalability of this approach and its application to larger, more complex QN topologies.
This paper presents a novel approach to designing adaptive quantum control policies using reinforcement learning (RL). Traditional methods for quantum control often rely on hand-designed pulse shapes or iterative optimization techniques, which can be computationally expensive and may not generalize well to different systems or desired operations. We propose a framework where an RL agent learns to dynamically adjust quantum control parameters in real-time, based on the system's state and the objective of achieving a specific quantum gate with high fidelity. The core of our approach lies in formulating the quantum control problem as a Markov Decision Process (MDP) and utilizing an RL algorithm to train a policy that maps system states to control parameters. We demonstrate the feasibility and effectiveness of this method through a theoretical analysis and a simplified numerical simulation. The resulting adaptive control policies offer a significant advantage over traditional methods, particularly for complex quantum systems and demanding fidelity requirements. The key contributions are the integration of RL for adaptive control and the formalization of the problem as an MDP.
Reinforcement learning (RL) faces significant challenges when dealing with environments characterized by delayed rewards and sparse feedback. Traditional RL algorithms often struggle to efficiently learn optimal policies in such scenarios due to the difficulty in associating actions with their eventual consequences. This research proposes a novel approach to RL leveraging quantum-inspired concepts – superposition and entanglement – to improve exploration and exploitation within the state space. The core idea involves designing a neural network architecture where nodes exist in a superposition of states, allowing the agent to concurrently evaluate multiple actions. Furthermore, entangled node interactions are implemented to facilitate the rapid and efficient transfer of information across the network, effectively addressing the problem of delayed rewards. We demonstrate, theoretically and through a conceptual framework, the potential benefits of this approach, highlighting its capacity to improve learning speed and robustness in complex RL environments. The network architecture is defined as follows: Let N=(n1,n2,...,nK) represent the neural network, where ni denotes the i-th node. Each node ni is associated with a state vector si=(si1,si2,...,sid) and an associated potential Vi. The superposition state of node ni is denoted as ∣ni⟩=αi∣0⟩+βi∣1⟩, where αi and βi are complex amplitudes. Entanglement is represented by a correlation function C(ni,nj) which quantifies the degree of interconnectedness between nodes ni and nj. The learning process involves updating the node potentials based on the expected future reward, considering the superposition state and entanglement interactions. The central focus is on the theoretical framework and the design principles, rather than a fully realized experimental implementation.
The integration of Quantum-Inspired Reinforcement Learning (QiRL) into Edge-Driven CyberPhysical Systems (CPS) presents a transformative approach to enhance real-time decision-making in distributed environments. While classical reinforcement learning (RL) struggles with scalability and computational complexity in edge-CPS, quantum-inspired Quantum-Inspired Reinforcement Learning for Edge-Driven Cyber-Physical Systems algorithms leverage superposition and entanglement principles to optimize policy exploration and convergence. This paper proposes a novel QiRL framework that combines quantum-inspired optimization with deep RL, enabling efficient resource allocation and adaptive control in latency-sensitive applications (e.g., smart grids, autonomous drones). We formalize the mathematical foundations of QiRL, design a hybrid neural-quantum architecture for edge deployment, and evaluate its performance under dynamic CPS constraints. Simulations on IoT-based case studies demonstrate 20−35% faster convergence and 15% higher energy efficiency compared to classical Deep Q-Networks (DQN). Key challenges, such as quantum noise simulation and edge-hardware limitations, are discussed to guide future research. This work bridges quantum computing principles with edge intelligence, offering a scalable pathway for nextgeneration CPS.