Improving qubit readout with hidden Markov models
Luis A. Martinez, Yaniv Rosen, Jonathan L. DuBois
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 or a support vector machine 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.