Quantum networks have the potential to completely transform current computation, communication, and sensing technologies by enabling the exchange of quantum data across vast distances. However, considerable hardware advances are needed before a practical quantum internet becomes a reality. Currently, one of the most significant challenges is effectively distributing entanglement among spatially separated network nodes. In this talk, I will present our work on finding improved protocols for entanglement distribution in a linear chain of nodes that take practical limitations into account. We use Reinforcement Learning approach to discover new protocols that offer improvement in terms of waiting time and fidelity of end-to-end entanglement. We believe that this improvement is the result of collaboration between the network nodes and provide quantifiers for it. Finally, I will present our approach of nested protocols to handle computationally costly long repeater chains.

Pratik is a PhD student in Quantum Science & Technology group at Louisiana State University. He completed his undergraduate and Master's degree in India and joined LSU for his PhD in 2018. Pratik is advised by Dr. Hwang Lee and mainly works in theoretical quantum optics and quantum computing.