Improving Qubit Readout with Hidden Markov Models
| Autor: | Martinez, Luis A., Rosen, Yaniv J., DuBois, Jonathan L. |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Phys. Rev. A 102, 062426 (2020) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1103/PhysRevA.102.062426 |
| Popis: | 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. Comment: 10 pages, 10 figures |
| Databáze: | arXiv |
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