Adaptive Entanglement Routing with Deep Q-Networks in Quantum Networks
This work addresses a persistent problem in quantum communication for enabling the quantum internet, but it appears incremental as it builds on existing reinforcement learning methods.
The study tackled the challenge of efficient qubit distribution in quantum networks by proposing a reinforcement learning-based adaptive entanglement routing framework, which optimized resource allocation and enhanced entanglement routing.
The quantum internet holds transformative potential for global communication by harnessing the principles of quantum information processing. Despite significant advancements in quantum communication technologies, the efficient distribution of critical resources, such as qubits, remains a persistent and unresolved challenge. Conventional approaches often fall short of achieving optimal resource allocation, underscoring the necessity for more effective solutions. This study proposes a novel reinforcement learning-based adaptive entanglement routing framework designed to enable resource allocation tailored to the specific demands of quantum applications. The introduced QuDQN model utilizes reinforcement learning to optimize the management of quantum networks, allocate resources efficiently, and enhance entanglement routing. The model integrates key considerations, including fidelity requirements, network topology, qubit capacity, and request demands.