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Topological quantum compiling with reinforcement learning
Physical Review Letters ( IF 8.385 ) Pub Date : 
Yuan-Hang Zhang; Pei-Lin Zheng; Yi Zhang; Dong-Ling Deng

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 open up a new avenue for intriguing applications of deep learning in quantum physics.
更新日期:2020-09-14

 

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