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Readout for superconducting transmon qubits involves the dispersive coupling of their energy levels to a detuned resonator, which is then probed with a resonant microwave tone. To extract qubit information from this microwave field, the field is amplified and demodulated to yield a pair of heterodyne signals that must be inverted to infer the corresponding measurement-conditioned qubit evolution in the form of stochastic quantum trajectories. This talk details the hardware implementation of multi-transmon chips and discusses several theoretical subtleties about the state trajectory inversion process from measured data. The talk also highlights recent experimental progress in using modern machine learning methods for automated calibration and tracking of the conditioned qubit evolution.
Readout for superconducting transmon qubits involves the dispersive coupling of their energy levels to a detuned resonator, which is then probed with a resonant microwave tone. To extract qubit information from this microwave field, the field is amplified and demodulated to yield a pair of heterodyne signals that must be inverted to infer the corresponding measurement-conditioned qubit evolution in the form of stochastic quantum trajectories. This talk details the hardware implementation of multi-transmon chips and discusses several theoretical subtleties about the state trajectory inversion process from measured data. The talk also highlights recent experimental progress in using modern machine learning methods for automated calibration and tracking of the conditioned qubit evolution.
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