Noise Adaptive Quantum Circuit Mapping Using Reinforcement Learning and Graph Neural Network

To generate physical quantum circuits, quantum gates are often added to the quantum circuit to satisfy the hardware constraints. This process is called quantum circuit mapping. Noise-aware mapping techniques generate physical quantum circuits for noisy intermediate-scale quantum (NISQ) computers. However, the absence of an accurate noise model of the quantum hardware limits its performance. In this article, we propose a noise adaptive quantum circuit mapping approach using reinforcement learning (RL) and graph neural network (GNN)-based reliability predictor. Our RL agent learns a quantum circuit mapping policy that significantly improves the quantum circuit output fidelity by interacting with the environment, which adopts a GNN reliability model that acts as quantum hardware and estimates the physical quantum circuit fidelity. Furthermore, we propose a multi-GNN reliability model to speed up the inference while maintaining high accuracy. Our proposed RL framework fills the gap between the simplified reliability models of the quantum hardware and the realistic noise impact of the quantum hardware on the quantum circuits. We demonstrate the improvement in the output fidelity of quantum circuits generated using our approach compared to other quantum circuit mapping techniques across different real-world quantum hardware using multiple-seed experiments.