Quantum Error Mitigation With Artificial Neural Network

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.

The Variational Quantum Eigensolver: a review of methods and best practices

“…8.2 [562]. Further, this idea has taken been further to develop artificial neural networks that predicts the noise, or the correction needed, on given quantum circuits [563,564].…”

$i$-QER: An Intelligent Approach towards Quantum Error Reduction

“…However, in practice, the implementation of quantum error correction imposes a huge burden in terms of the required number of qubits, which remains beyond the capabilities of present near-term devices. Due to the typical error rate of current near-term devices, various error reduction techniques have emerged; one of them is quantum error mitigation (QEM) [20,21]. QEM does not use any extra quantum resources, rather it aims to slightly enhance the accuracy of estimating the outcome in a given quantum computational problem through several techniques such as extrapolation, probabilistic error cancellation, quantum subspace expansion, symmetry verification, machine learning etc.…”

Modelling for Quantum Error Mitigation

“…For instance, in [19] the authors propose a way to mitigate readout error in the context of computing expectation values of observables by modifying the measured operators accordingly. Moreover, it has been suggested to use machine learning in quantum error mitigation [22], [23], resulting in a very diverse field of research.…”