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Learning Temporal Correlations in Continuous Quantum Measurements

Thursday April 22, 2021
3:30 pm


 Presenter:  Justin Dressel, Chapman University
 Series:  CQuIC Seminars
 Abstract:  Modern superconducting hardware for quantum computation measures information about qubit energy states by entangling them with distinct evolution frequencies of a standing mode in a microwave resonator. Monitoring and filtering the scattered microwave field enables the measurement and reconstruction of qubit state evolution, which can be used to extract average information about its coherent dynamics and potentially diagnose systematic errors in controllable gates. However, traditional Markovian filtering methods like stochastic master equations become inaccurate when monitoring rapid evolution, as needed for gates. We experimentally demonstrate an alternative reconstruction approach using a Long-Short Term Memory (LSTM) recurrent neural network (RNN) that correctly learns the temporal correlations present in the continuous readout. Without making prior assumptions about the dynamics, this method automatically corrects for finite bandwidth signal delay and accurately identifies subtle quantum effects like drive-hybridization of measurement eigenstates, changing measurement rates due to resonator entanglement dynamics, and dissipation-induced drive detuning.
 Host:  Pablo Poggi
 Location:  Zoom

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