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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Improving dispersive readout of a superconducting qubit by machine learning on path signature

Shuxiang Cao · Zhen Shao · Jian-Qing Zheng · Mustafa Bakr · Peter Leek · Terry Lyons


One major challenge that arises from quantum computing is to implement fast, high-accuracy quantum state readout. For superconducting circuits, this problem reduces to a time series classification problem on readout signals. We propose that using path signature methods to extract features can enhance existing techniques for quantum state discrimination. We demonstrate the superior performance of our proposed approach over conventional methods in distinguishing three different quantum states on real experimental data from a superconducting transmon qubit.

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