Quantum networks attract increasing attention due to their unique advantages over classical networks. However, the challenge of efficiently and economically constructing reliable wide-area quantum networks remains unresolved. This problem, termed network planning, is typically modeled as an Integer Linear Programming (ILP) problem. Existing solver-based solutions for classical networks face scalability issues, while heuristic approaches that sacrifice optimality for scalability heavily rely on human expertise and lack generalizability. Recently, deep reinforcement learning (DRL) has demonstrated potential in adaptive decision-making for classical network planning. However, quantum networks introduce unique constraints due to their end-to-end entanglement distribution requirements. To address this, we propose a two-stage quantum network resource allocation framework, QPLAN, that combines multi-agent DRL with traditional optimization solvers. Our approach uses a novel multi-agent reinforcement learning strategy using graph neural networks (GNNs) and transformers for traffic flow and corresponding hardware resource allocation to minimize hardware cost while cooperatively satisfying problem constraints as much as possible. The output of the DRL algorithm is then used as an initial input to prune the solution space, "warm-starting" the ILP solver and further improving the solution. This hybrid approach effectively reduces manual effort and enhances scalability. Experimental results show that our method yields near-optimal solutions with better scalability than only using ILP solvers.
Quantum networks attract increasing attention due to their unique advantages over classical networks. However, the challenge of efficiently and economically constructing reliable wide-area quantum networks remains unresolved. This problem, termed network planning, is typically modeled as an Integer Linear Programming (ILP) problem. Existing solver-based solutions for classical networks face scalability issues, while heuristic approaches that sacrifice optimality for scalability heavily rely on human expertise and lack generalizability. Recently, deep reinforcement learning (DRL) has demonstrated potential in adaptive decision-making for classical network planning. However, quantum networks introduce unique constraints due to their end-to-end entanglement distribution requirements. To address this, we propose a two-stage quantum network resource allocation framework, QPLAN, that combines multi-agent DRL with traditional optimization solvers. Our approach uses a novel multi-agent reinforcement learning strategy using graph neural networks (GNNs) and transformers for traffic flow and corresponding hardware resource allocation to minimize hardware cost while cooperatively satisfying problem constraints as much as possible. The output of the DRL algorithm is then used as an initial input to prune the solution space, "warm-starting" the ILP solver and further improving the solution. This hybrid approach effectively reduces manual effort and enhances scalability. Experimental results show that our method yields near-optimal solutions with better scalability than only using ILP solvers.
With the continuous expansion of quantum communication networks and the increasing diversity of their business types, how to achieve efficient configuration under limited quantum resources has become the key to improving system performance. The quantum resources described in this article mainly include entanglement pairs, quantum bits, and channel capacity. Traditional optimization methods typically rely on precise mathematical models, which are difficult to adapt to dynamically evolving network environments and susceptible to noise interference. Therefore, this study proposes a machine learning based framework for optimizing the allocation of quantum communication resources. This framework integrates deep reinforcement learning and graph neural network technology, enabling collaborative optimization of entanglement allocation, routing selection, and time slice scheduling between nodes. Three typical quantum communication scenarios were constructed and tested in a simulation environment, including static topology, dynamic links, and high noise environments. The proposed method was compared and analyzed with integer linear programming, heuristic greedy algorithms, and rule-based strategies. Experimental data shows that this research method achieved an improvement of approximately 12% to 18% in average fidelity retention, a 15% to 22% increase in resource utilization, and a 20% reduction in latency. This study provides useful reference for the intelligent resource management of large-scale quantum networks in the future.
This paper explores the application of Deep Reinforcement Learning (DRL) for optimizing quantum resource allocation in quantum computing. Traditional quantum resource allocation often relies on manual design and expert knowledge, which can be computationally expensive and limit scalability. We propose a novel framework utilizing DRL to automate this process, learning optimal strategies for allocating quantum resources (qubits, gates, and time) to maximize the performance of quantum algorithms. The core of our approach involves constructing a DRL model where the agent learns to make decisions regarding resource allocation based on the current state of the quantum system and the desired algorithm outcome. We define a state space representing the quantum system's configuration, action space encompassing various resource allocation options, and a reward function that reflects the algorithm's success. Through this framework, we aim to significantly enhance the efficiency and performance of quantum computations. This work presents a foundational approach, paving the way for more sophisticated DRL-based quantum resource allocation strategies.
The significant rise in wireless communication, driven by sixth-generation (6G) networks, underscores the urgent need for innovative resource allocation methods to achieve efficiency, scalability, and reliability. This survey explores the integration of Quantum Deep Reinforcement Learning (QDRL) as a strategy to address the challenges posed by dynamic and complex wireless environments. By leveraging quantum computing features such as superposition and entanglement, QDRL enhances computational efficiency and mitigates the scalability limitations of classical and deep reinforcement learning (DRL) in high-dimensional optimization. The study reviews recent QDRL approaches for key resource allocation tasks, including power distribution, user scheduling, and channel assignment, and examines quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Neural Networks (QNN) for improved decision-making in wireless systems. Furthermore, it emphasizes the connection between quantum computing and DRL, highlighting hybrid architectures that balance computational cost, energy efficiency, and scalability. Finally, the survey identifies research gaps and outlines future directions, paving the way for robust QDRL strategies to support next-generation wireless networks.
Quantumcomputers exhibit significant potential for addressing problems that are intractable by classical computing. Nevertheless, the scalability of quantum architectures remains a pivotal challenge within the field. Current efforts for increasing the number of qubits. However, using these architectures implies dealing with new hardware and algorithmic challenges. The primary focus of this thesis is the partitioning of quantum circuits across multiple cores; a task classified as an NP-hard problem. While classical algorithms often struggle with execution time and scalability, Machine Learning (ML) has emerged as a promising, faster yet powerful alternative. This thesis presents a novel approach for circuit partitioning in multi-core quantum architectures using Deep Reinforcement Learning (DRL) with graph neural networks (GNN). This methodology provides a flexible and efficient framework, enabling the partitioning of circuits with varying numbers of qubits while maintaining high performance and scalability.
The vehicle routing problem (VRP) is a fundamental NP-hard task in intelligent transportation systems with broad applications in logistics and distribution. Deep reinforcement learning (DRL) with Graph Neural Networks (GNNs) has shown promise, yet classical models rely on large multi-layer perceptrons (MLPs) that are parameter-heavy and memory-bound. We propose a Quantum Graph Attention Network (Q-GAT) within a DRL framework, where parameterized quantum circuits (PQCs) replace conventional MLPs at critical readout stages. The hybrid model maintains the expressive capacity of graph attention encoders while reducing trainable parameters by more than 50%. Using proximal policy optimization (PPO) with greedy and stochastic decoding, experiments on VRP benchmarks show that Q-GAT achieves faster convergence and reduces routing cost by about 5% compared with classical GAT baselines. These results demonstrate the potential of PQC-enhanced GNNs as compact and effective solvers for large-scale routing and logistics optimization.
Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation