Machine Learning based Discrimination for Excited State Promoted Readout
A limiting factor for readout fidelity for superconducting qubits is the
relaxation of the qubit to the ground state before the time needed for the
resonator to reach its final target state. A technique known as excited state
promoted (ESP) readout was proposed to reduce this effect and further improve
the readout contrast on superconducting hardware. In this work, we use readout
data from IBM's five-qubit quantum systems to measure the effectiveness of
using deep neural networks, like feedforward neural networks, and various
classification algorithms, like k-nearest neighbors, decision trees, and
Gaussian naive Bayes, for single-qubit and multi-qubit discrimination. These
methods were compared to standardly used linear and quadratic discriminant
analysis algorithms based on their qubit-state-assignment fidelity performance,
robustness to readout crosstalk, and training time.
Utkarsh Azad Helena Zhang