Using Projective Simulation And Reinforcement Learning For Quantum Circuit Discovery And Optimization

Machine Learning (ML) algorithms are being applied in many fields and with every passing day more applications in areas such as business, healthcare, and science seem to be added to the ever-growing list. Recently, this list also started to include more and more applications from the field of quantum science, such as, e.g., quantum many-body systems, quantum optics, quantum chemistry, quantum material science, and quantum algorithms. We here investigate the potential of ML algorithms to drive progress in quantum information science, specifically quantum communication. In particular, we study if it is possible for a ML algorithm to self-learn optimal strategies for entanglement generation and long-distance distribution. High-fidelity, long-distance entanglement is a key requirement for quantum communication, specifically the realization of a long-distance quantum network (quantum internet). We will discuss our efforts to use a projective-simulation-based reinforcement algorithm to identify successful entanglement generation protocols to enable long-distance quantum communication.