  Measurement for qubits plays a key role in quantum computation. Current
methods for classifying states of single qubit in a superconducting multi-qubit
system produce fidelities lower than expected due to the existence of
crosstalk, especially in case of frequency crowding. Here, We make the digital
signal processing (DSP) system used in measurement into a shallow neural
network and train it to be an optimal classifier to reduce the impact of
crosstalk. The experiment result shows the crosstalk-induced readout error
deceased by 100% after a 3-second optimization applied on the 6-qubit
superconducting quantum chip.
