Suppressing Measurement Noise in Logical Qubits Through Measurement Scheduling

TL;DR

This paper introduces a dynamic measurement scheduling protocol using reinforcement learning to reduce logical readout errors in quantum error correction, improving fidelity and robustness in noisy quantum systems.

Contribution

It presents a novel adaptive measurement scheduling method that redistributes measurement tasks to suppress noise, leveraging reinforcement learning for real-time optimization.

Findings

Logical error rates reduced by up to 34% across code distances 3 to 11.

Enhanced robustness in systems dominated by measurement noise.

Demonstrated effectiveness through numerical simulations.

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.