Improving Qubit Readout with Hidden Markov Models

TL;DR

This paper introduces a hidden Markov model-based approach for qubit readout that improves classification fidelity and eliminates the need for readout time optimization, offering a robust and insightful analysis of qubit state dynamics.

Contribution

The paper presents a novel application of hidden Markov models for qubit readout, surpassing traditional methods in fidelity and providing a comprehensive framework for studying qubit state transitions.

Findings

Higher state assignment fidelity than MVG and SVM methods

Eliminates qubit-dependent readout time optimization

Estimates fidelities reaching the ideal limit

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.