Reinforcement Learning for Fast and Robust Longitudinal Qubit Readout

Quantum Physics [Submitted on 18 Mar 2026] Reinforcement Learning for Fast and Robust Longitudinal Qubit Readout Yiming Yu, Yuan Qiu, Xinyu Zhao, Ye-Hong Chen, Yan Xia 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. Comments: 11 pages, 5 figures Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2603.18060 [quant-ph]   (or arXiv:2603.18060v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2603.18060 Focus to learn more Submission history From: Ye-Hong Chen Dr. [view email] [v1] Wed, 18 Mar 2026 01:27:09 UTC (2,053 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-03 References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)