Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers

• URL: http://arxiv.org/abs/2007.15957v1
• Date: Fri, 31 Jul 2020 10:57:24 GMT
• Title: Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers
• Authors: Matteo G. Pozzi (1), Steven J. Herbert (1 and 2), Akash Sengupta (3), Robert D. Mullins (1) ((1) University of Cambridge Computer Laboratory, (2) Cambridge Quantum Computing, (3) Department of Engineering, University of Cambridge)
• Abstract summary: We propose a qubit routing procedure that uses a modified version of the deep Q-learning paradigm. The system is able to outperform the qubit routing procedures from two of the most advanced quantum compilers currently available.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: "Qubit routing" refers to the task of modifying quantum circuits so that they satisfy the connectivity constraints of a target quantum computer. This involves inserting SWAP gates into the circuit so that the logical gates only ever occur between adjacent physical qubits. The goal is to minimise the circuit depth added by the SWAP gates. In this paper, we propose a qubit routing procedure that uses a modified version of the deep Q-learning paradigm. The system is able to outperform the qubit routing procedures from two of the most advanced quantum compilers currently available, on both random and realistic circuits, across near-term architecture sizes.

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