Suppressing measurement noise in logical qubits through measurement scheduling
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Abstract
Quantum error correction is essential for reliable quantum computation, where surface codes demonstrate high fault-tolerant thresholds and hardware efficiency. However, noise in single-shot measurements limits logical readout fidelity, forming a critical bottleneck for fault-tolerant quantum computation. We propose a dynamic measurement scheduling protocol that suppresses logical readout errors by adaptively redistributing measurement tasks from error-prone qubits to stable nodes. Using shallow entangled circuits, the protocol balances gate errors and measurement noise. This is achieved by dynamically prioritizing resource allocation based on topological criticality and error metrics. When addressing realistic scenarios where temporal constraints are governed by decoherence limits and error-correction requirements, we implement reinforcement learning (RL) to achieve adaptive measurement scheduling. Numerical simulations show that logical error rates can be reduced by up to 34% across code distances for 3 to 11, with enhanced robustness in measurement-noise-dominated systems. Our protocol offers a versatile, hardware-efficient solution for high-fidelity quantum error correction, advancing large-scale quantum computing.