Graph Neural Network Predicts Qubit Routing Costs
A graph neural network has been trained to predict routing costs for qubit allocation on a fault-tolerant chip layout. The model learns from the connectivity graph of the quantum device and the required two-qubit interactions to estimate the cost of moving quantum information, a step that compilers usually handle with hand-built heuristics. The work presents this learned cost predictor as a way to automate part of the qubit mapping and routing process.
OutlookPlausible
Within two years, learned routing-cost predictors could be integrated as optional modules in open-source quantum compilers like Qiskit, tket, or Cirq.