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arXiv · 2006.00109

Improving Qubit Readout with Hidden Markov Models

Luis A. Martinez·Yaniv J. Rosen·Jonathan L. DuBois
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Abstract

We demonstrate the application of pattern recognition algorithms via hidden Markov models (HMM) for qubit readout. This scheme provides a state-path trajectory approach capable of detecting qubit state transitions and makes for a robust classification scheme with higher starting state assignment fidelity than when compared to a multivariate Gaussian (MVG) or a support vector machine (SVM) scheme. Therefore, the method also eliminates the qubit-dependent readout time optimization requirement in current schemes. Using a HMM state discriminator we estimate fidelities reaching the ideal limit. Unsupervised learning gives access to transition matrix, priors, and IQ distributions, providing a toolbox for studying qubit state dynamics during strong projective readout.

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Luis A. Martinez, Yaniv J. Rosen, Jonathan L. DuBois. 2020-10-31. Improving Qubit Readout with Hidden Markov Models. https://doi.org/10.1103/physreva.102.062426

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