Reinforcement Learning for Fast and Robust Longitudinal Qubit Readout

TL;DR

This paper introduces a reinforcement learning approach to optimize longitudinal qubit readout pulses, achieving significant improvements in signal-to-noise ratio and robustness within hardware constraints.

Contribution

It develops a reinforcement learning framework combined with shortcuts to adiabaticity to design optimized, hardware-compatible control pulses for quantum nondemolition readout.

Findings

50% SNR improvement over baseline

Robustness to parameter drifts

Smooth, hardware-compatible waveforms

Abstract

Longitudinal coupling offers a compelling pathway for quantum nondemolition (QND) readout, but pulse design is constrained by hardware limitations such as the coupling strength and the photon number required to stay within the linear regime. We develop a reinforcement learning framework to optimize the longitudinal coupling waveform under such constraints. Building upon the theoretical foundation of shortcuts to adiabaticity (STA), we parameterize an auxiliary trajectory with cubic B-splines and reconstruct the physical control. At a fixed short readout time, the optimized pulse converges to a constraint saturating flat-top protocol and yields a approximately $50\%$ improvement in $\mathrm{SNR}$ over an STA baseline, while exhibiting enhanced robustness to parameter drifts. Simulation results demonstrate the efficacy of reinforcement learning in optimizing longitudinal readout pulses. The optimized protocol attains substantial performance gains and yields smooth, hardware-compatible waveforms governed by an interpretable ``saturate-and-hold'' mechanism.