IBM

AI-Based Techniques to Assign Physical Qubits in Quantum Algorithms

Quantum processors require each circuit’s virtual qubits to be assigned to specific physical qubits. That mapping can significantly influence algorithm performance because device noise, limited qubit availability, and hardware connectivity constraints may affect execution quality. This project aimed to explore an AI-based qubit mapping capability for this combinatorially large optimization problem using an AlphaZero-style reinforcement learning approach. The student team aimed to support identification of qubit assignments that could better align with processor constraints and potentially reduce the noise experienced by a circuit.