Graph Neural Network Predicts Qubit Routing Costs

Tian Li, Tan Li, and Wansu Bao of Henan Key Laboratory of Quantum Information and Cryptography have developed a graph-based reinforcement-learning framework to address logical qubit allocation, a critical compilation problem for fault-tolerant quantum architectures. The work demonstrates a method for assigning circuit qubits to chip tiles while minimizing the ancilla-qubit cost, the number of extra workspace qubits needed for circuit execution, and maintaining access to essential quantum resources. Evaluated on MQTBench circuits, the allocator reduces average ancilla-qubit cost by 36.7% compared to the ECMAS+ baseline, achieving lower costs in 57 of 64 qubit-size bins and establishing learned allocation as a scalable paradigm.

Graph Neural Network Predicts Allocation-Aware Circuit Costs

A newly developed graph neural network (GNN) predicts the ancillary qubit costs associated with quantum circuit allocation, offering a significant step toward more efficient use of limited quantum resources. The framework, detailed in recent work, moves beyond traditional circuit mapping by learning to anticipate the demand for these extra workspace qubits, often described as before allocation even begins. This predictive capability stems from pre-training the GNN on a supervised task, estimating ancilla-qubit costs from circuit-allocation pairs represented as allocation-aware circuit graphs.