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Topological Quantum Compiling with Reinforcement Learning

发表会议及期刊:Physical Review Letter

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.

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