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Noisy Intermediate-Scale Quantum Computation Algorithms : machine learning, secure cloud computing, quantum annealing, and error mitigation

Yuta Shingu

Published Mar 18, 2025• 0 citations
Publisher
DOI
10.20604/0002000210

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Noisy intermediate-scale quantum computation and its application (審査報告)

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Kernel methods are the basis of most classical machine learning algorithms such as Gaussian Process (GP) and Support Vector Machine (SVM). Computing kernels using noisy intermediate scale quantum (NISQ) devices has attracted considerable attention due to recent progress in the design of NISQ devices. However noise and errors on current NISQ devices can negatively affect the predicted kernels. In this paper we utilize two quantum kernel machine learning (ML) algorithms to predict the labels of a Breast Cancer dataset on two different NISQ devices: quantum kernel Gaussian Process (qkGP) and quantum kernel Support Vector Machine (qkSVM). We estimate the quantum kernels on the 11 qubit IonQ and the 5 qubit IBMQ Belem quantum devices. Our results demonstrate that the predictive performances of the error mitigated quantum kernel machine learning algorithms improve significantly compared to their non-error mitigated counterparts. On both NISQ devices the predictive performances became comparable to those of noiseless quantum simulators and their classical counterparts
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.
Quantum computers, while offering exponential computational advantages, are highly vulnerable to noise-induced errors, particularly during the measurement process. This paper investigates machine learning-based error mitigation strategies with a focus on transfer learning. By initially training a base model on a specific quantum device, we leverage transfer learning to adapt the model for use on different quantum computers. This approach not only achieves superior performance with reduced dataset size and training time but also maintains hardware agnosticism, as demonstrated through tests on various IBM quantum backends. Our method provides a practical solution for enhancing the accuracy and efficiency of noisy intermediate-scale quantum (NISQ) algorithms by improving machine learning-based error mitigation.