Distributing entanglement over long distances is one of the central tasks in quantum networks. An important problem, especially for near-term quantum networks, is to develop optimal entanglement distribution protocols that take into account the limitations of current and near-term hardware, such as quantum memories with limited coherence time. In this talk, I discuss how the framework of Markov decision processes can be used to address this problem. First, using this framework, I show how to obtain optimal policies for elementary links in the short-term and long-term limits. I then extend these considerations to chains of quantum repeaters. I show that the framework of Markov decision processes provides a systematic way of determining policies that are optimal (in terms of figures of merit such as fidelity and waiting time), and I also show that it can be used to address other important considerations in near-term quantum networks, such as policies that take local or global knowledge of the network into account.
Join Zoom Meeting
INQNET (INtelligent Quantum NEtworks & Technologies, inqnet.caltech.edu) is a research program that aims to bring together academia, national laboratories, and industry to advance quantum science and technology and address relevant fundamental questions in physics.