Quantum readout error mitigation via deep learning

TL;DR

This paper introduces a deep learning-based method to mitigate quantum readout errors without extra qubits or gates, improving accuracy on IBM quantum devices by correcting non-linear noise.

Contribution

It presents a novel neural network approach for quantum readout error mitigation that outperforms existing linear methods on real hardware.

Findings

Effective reduction of readout errors demonstrated on IBM five-qubit devices

Neural network corrects non-linear noise beyond linear inversion methods

Improved measurement accuracy enhances quantum algorithm reliability

Abstract

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.