Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networks
This addresses the critical issue of noise in quantum computing for researchers and practitioners, though it is incremental as it builds on existing optimization techniques with a novel method.
The paper tackles the problem of reducing two-qubit gates in quantum circuits to mitigate noise on quantum hardware, introducing a framework that combines ZX calculus, graph neural networks, and reinforcement learning to achieve significant minimization of CNOT gates, demonstrating competitiveness with state-of-the-art optimizers on diverse random circuits.
Quantum computing is currently strongly limited by the impact of noise, in particular introduced by the application of two-qubit gates. For this reason, reducing the number of two-qubit gates is of paramount importance on noisy intermediate-scale quantum hardware. To advance towards more reliable quantum computing, we introduce a framework based on ZX calculus, graph-neural networks and reinforcement learning for quantum circuit optimization. By combining reinforcement learning and tree search, our method addresses the challenge of selecting optimal sequences of ZX calculus rewrite rules. Instead of relying on existing heuristic rules for minimizing circuits, our method trains a novel reinforcement learning policy that directly operates on ZX-graphs, therefore allowing us to search through the space of all possible circuit transformations to find a circuit significantly minimizing the number of CNOT gates. This way we can scale beyond hard-coded rules towards discovering arbitrary optimization rules. We demonstrate our method's competetiveness with state-of-the-art circuit optimizers and generalization capabilities on large sets of diverse random circuits.