  Quantum computing devices are inevitably subject to errors. To leverage
quantum technologies for computational benefits in practical applications,
quantum algorithms and protocols must be implemented reliably under noise and
imperfections. Since noise and imperfections limit the size of quantum circuits
that can be realized on a quantum device, developing quantum error mitigation
techniques that do not require extra qubits and gates is of critical
importance. In this work, we present a deep learning-based protocol for
reducing readout errors on quantum hardware. Our technique is based on training
an artificial neural network with the measurement results obtained from
experiments with simple quantum circuits consisting of singe-qubit gates only.
With the neural network and deep learning, non-linear noise can be corrected,
which is not possible with the existing linear inversion methods. The advantage
of our method against the existing methods is demonstrated through quantum
readout error mitigation experiments performed on IBM five-qubit quantum
devices.
