Topological Quantum Compiling with Reinforcement Learning

Yuan-Hang Zhang, Pei Lin Zheng, Yi Zhang, Dong-Ling Deng

Abstract:

Quantum compiling, a process that decomposes the quantum algorithm into a series of hardware
compatible commands or elementary gates, is of fundamental importance for quantum computing. We
introduce an efficient algorithm based on deep reinforcement learning that compiles an arbitrary single
qubit gate into a sequence of elementary gates from a finite universal set. It generates near-optimal gate
sequences with given accuracy and is generally applicable to various scenarios, independent of the
hardware-feasible universal set and free from using ancillary qubits. For concreteness, we apply this
algorithm to the case of topological compiling of Fibonacci anyons and obtain near-optimal braiding
sequences for arbitrary single-qubit unitaries. Our algorithm may carry over to other challenging quantum
discrete problems, thus opening up a new avenue for intriguing applications of deep learning in quantum
physics.