Quantum Error Mitigation With Artificial Neural Network

Changjun Kim,Kyungdeock Daniel Park,June-Koo Rhee

doi:10.1109/access.2020.3031607

Abstract

A quantum error mitigation technique based on machine learning is proposed, which learns how to adjust the probabilities estimated by measurement in the computational basis. Neural networks in two different designs are trained with random quantum circuits consisting of a set of one- and two-qubit unitary gates whose measurement statistics in the ideal (noiseless) and real (noisy) cases are known. Once the neural networks are trained, they infer the amount of probability adjustment to be made on the measurement obtained from executing an unseen quantum circuit to reduce the error. The proposed schemes are tested with two-, three-, five-, and seven-qubit quantum circuits of depth up to 20 by computer simulations with realistic error models and experiments using the IBM quantum cloud platform. In all test cases, the proposed mitigation technique reduces the error effectively. Our method can be used to improve the accuracy of noisy intermediate-scale quantum (NISQ) algorithms without relying on extensive error characterization or quantum error correction.

Highlights

The development of full-fledged quantum computers that promise revolutionary opportunities is being challenged due to computational errors that occur when theoretical ideas are implemented on real quantum devices
We propose and investigate two different designs of machine learning methods, namely an artificial neural network (ANN) and a concatenated artificial neural network (Concatenated ANN)
We developed a quantum error mitigation scheme based on classical machine learning methods

Summary

Introduction

The development of full-fledged quantum computers that promise revolutionary opportunities is being challenged due to computational errors that occur when theoretical ideas are implemented on real quantum devices. The typical error rate of current quantum devices is near or above the fault-tolerance threshold, hindering the power of quantum error correction. With this background, the idea of quantum error mitigation (QEM) emerged recently. Unlike QEC, QEM does not necessarily aim to fully remove the entropy increased by unwanted interaction with the environment to recover the logical state. Instead, it aims to merely improve the accuracy of estimating the final answer in a given computational task without having to encode logical quantum state in a multi-qubit entangled state. Since QEM does not require extra quantum resources, it is expected to improve the quantum computation to some extent even when the error

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