Deep Reinforcement Learning for Mapping Quantum Circuits to 2D Nearest-Neighbor Architectures

Recently, quantum computing is considered as a promising future computing paradigm. However, when implementing quantum circuits on a quantum device, it is necessary to ensure that quantum circuits satisfy nearest-neighbor architecture constraints. When performing nearest-neighbor architecture mapping for quantum circuits, it is inevitable to introduce SWAP gates, which will increase the overhead and reduce the fidelity. Therefore, it is crucial to complete the mapping with the minimum SWAP. In this paper, a 2D nearest-neighbor architecture mapping method for quantum circuits is proposed based on deep reinforcement learning. In the initial mapping, an isomorphic graph initial mapping search algorithm is designed to quickly find the isomorphic graph between quantum circuit and architecture constraint graph. For quantum circuits without isomorphic graph mapping, the reordering algorithm of qubit impact factors is designed to obtain the initial placement position of qubits. In the SWAP gate addition, a qubit local reordering algorithm based on dueling deep-Q-network is designed to reduce SWAP gates. Experiments on the benchmark set B131 and IBM Q20 Tokyo verify that the proposed method can add fewer SWAP gates. Compared with the state-of-the-art method, the average running time is accelerated by 63.1%, and the average SWAP gates added are reduced by 24.7%. Quantum circuit mapping refers to the task of modifying quantum circuits so that they meet the connectivity constraints of the target quantum computer. A mapping method based on deep reinforcement learning is proposed to automatically minimize the number of SWAP gate additions. Compared with current state-of-the-art methods, the average number of SWAP gates added is reduced by 24.7%.image