  Distributed quantum information processing is essential for building quantum
networks and enabling more extensive quantum computations. In this regime,
several spatially separated parties share a multipartite quantum system, and
the most natural set of operations are Local Operations and Classical
Communication (LOCC). As a pivotal part in quantum information theory and
practice, LOCC has led to many vital protocols such as quantum teleportation.
However, designing practical LOCC protocols is challenging due to LOCC's
intractable structure and limitations set by near-term quantum devices. Here we
introduce LOCCNet, a machine learning framework facilitating protocol design
and optimization for distributed quantum information processing tasks. As
applications, we explore various quantum information tasks such as entanglement
distillation, quantum state discrimination, and quantum channel simulation. We
discover novel protocols with evident improvements, in particular, for
entanglement distillation with quantum states of interest in quantum
information. Our approach opens up new opportunities for exploring entanglement
and its applications with machine learning, which will potentially sharpen our
understanding of the power and limitations of LOCC.
