Quantum Virtual Link Generation via Reinforcement Learning
Résumé
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.
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