Reinforcement Learning for Optimizing Large Qubit Array based Quantum Sensor Circuits
This addresses the challenge of optimizing large-scale quantum circuits for quantum sensors, which is critical for enhancing sensitivity and efficiency, though it appears incremental as it combines existing methods (reinforcement learning and tensor networks) in a new application.
The paper tackles the problem of designing and controlling quantum sensor circuits with many qubits, which becomes exponentially complex as qubit count increases, by using reinforcement learning with tensor-network simulation to optimize circuits with up to 60 qubits, achieving QFI values near 1, entanglement entropy of 0.8-1.0, and up to 90% reductions in depth and gate count.
As the number of qubits in a sensor increases, the complexity of designing and controlling the quantum circuits grows exponentially. Manually optimizing these circuits becomes infeasible. Optimizing entanglement distribution in large-scale quantum circuits is critical for enhancing the sensitivity and efficiency of quantum sensors [5], [6]. This paper presents an engineering integration of reinforcement learning with tensor-network-based simulation (MPS) for scalable circuit optimization for optimizing quantum sensor circuits with up to 60 qubits. To enable efficient simulation and scalability, we adopt tensor network methods, specifically the Matrix Product State (MPS) representation, instead of traditional state vector or density matrix approaches. Our reinforcement learning agent learns to restructure circuits to maximize Quantum Fisher Information (QFI) and entanglement entropy while reducing gate counts and circuit depth. Experimental results show consistent improvements, with QFI values approaching 1, entanglement entropy in the 0.8-1.0 range, and up to 90% reduction in depth and gate count. These results highlight the potential of combining quantum machine learning and tensor networks to optimize complex quantum circuits under realistic constraints.