Topological Quantum Compiling with Reinforcement Learning

• URL: http://arxiv.org/abs/2004.04743v2
• Date: Wed, 21 Oct 2020 05:46:38 GMT
• Title: Topological Quantum Compiling with Reinforcement Learning
• Authors: Yuan-Hang Zhang, Pei-Lin Zheng, Yi Zhang and Dong-Ling Deng
• Abstract summary: We introduce an efficient algorithm that compiles an arbitrary single-qubit gate into a sequence of elementary gates from a finite universal set. 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.
• Score: 7.741584909637626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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