Mitigating Crosstalk-Induced Qubit Readout Error with Shallow-Neural-Network Discrimination
Peng Duan, Zifeng Chen, Qi Zhou, Weicheng Kong, Hai-Feng Zhang, Guo‐Ping Guo
Abstract
Measurement of qubits plays a key role in quantum computation. In superconducting multiqubit quantum processors, a multiplexed readout scheme is widely used. In such a scheme, measurement of a qubit state may be influenced by the state of neighboring qubits, due to various crosstalk effects, which will degrade the readout fidelity. To reduce the impact of crosstalk, we model the digital signal processing system used in measurements as a shallow neural network and train it to become a state discriminator. Applying our method to a six-qubit superconducting quantum chip, we see an overall improved readout performance compared with a contemporary qubit-state discriminator. The readout crosstalk is decreased by more than $80\mathrm{%}$. The training and optimization process of the neural network consumes only about 10 s.