Journal
PHYSICAL REVIEW A
Volume 102, Issue 6, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevA.102.062426
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Funding
- U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
- Department of Energy Office of Advanced Scientific Computing Research, Quantum Testbed Pathfinder Program [2017-LLNL-SCW163]
- National Nuclear Security Administration Advanced Simulation and Computing Beyond Moore's Law program [LLNL-ABS-795437]
- Lab Directed Research and Development award [LDRD19-ERD-013, LLNL-JRNL-810931]
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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.
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