  Single-shot readout is a key component for scalable quantum information
processing. However, many solid-state qubits with favorable properties lack the
single-shot readout capability. One solution is to use the repetitive
quantum-non-demolition readout technique, where the qubit is correlated with an
ancilla, which is subsequently read out. The readout fidelity is therefore
limited by the back-action on the qubit from the measurement. Traditionally, a
threshold method is taken, where only the total photon count is used to
discriminate qubit state, discarding all the information of the back-action
hidden in the time trace of repetitive readout measurement. Here we show by
using machine learning (ML), one obtains higher readout fidelity by taking
advantage of the time trace data. ML is able to identify when back-action
happened, and correctly read out the original state. Since the information is
already recorded (but usually discarded), this improvement in fidelity does not
consume additional experimental time, and could be directly applied to
preparation-by-measurement and quantum metrology applications involving
repetitive readout.
