Topological Quantum Compiling with Reinforcement Learning

Yuan-Hang Zhang , 1,2 Pei-Lin Zheng , 3,4 Yi Zhang , 3,4,* and Dong-Ling Deng1,5,†

¹Center for Quantum Information, IIIS, Tsinghua University, Beijing 100084, People’s Republic of China

²Department of Physics, University of California, San Diego, California 92093, USA

³International Center for Quantum Materials, Peking University, Beijing 100871, China

⁴School of Physics, Peking University, Beijing 100871, China

⁵Shanghai Qi Zhi Institute, 41th Floor, AI Tower, No. 701 Yunjin Road, Xuhui District, Shanghai 200232, China 

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 learnin in quantum physics.