Qubit Routing using Graph Neural Network aided Monte Carlo Tree Search
This addresses a critical bottleneck in quantum computing compilation for near-term hardware, offering a practical improvement over existing methods.
The paper tackles the problem of qubit routing for quantum circuits to satisfy hardware connectivity constraints by proposing an architecture-agnostic method that minimizes circuit depth, outperforming other routing implementations on various benchmarks.
Near-term quantum hardware can support two-qubit operations only on the qubits that can interact with each other. Therefore, to execute an arbitrary quantum circuit on the hardware, compilers have to first perform the task of qubit routing, i.e., to transform the quantum circuit either by inserting additional SWAP gates or by reversing existing CNOT gates to satisfy the connectivity constraints of the target topology. We propose a procedure for qubit routing that is architecture agnostic and that outperforms other available routing implementations on various circuit benchmarks. The depth of the transformed quantum circuits is minimised by utilizing the Monte Carlo tree search to perform qubit routing, aided by a Graph neural network that evaluates the value function and action probabilities for each state.