Quantum machine learning with Qiskit: Evaluating regression accuracy and noise impact

Quantum machine learning (QML) can be employed in solving complicated machine learning tasks although the performance in examining the regression processes is only barely understood. Knowledge gaps are intended to be closed by studying modelling performance of QML in regression tasks, with emphasis...

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Bibliographic Details
Published in:IET quantum communication 2024-07, Vol.5 (4), p.310-321
Main Authors: Kumar, Amit, Sharma, Neha, Marriwala, Nikhil Kumar, Panda, Sunita, Aruna, M., Kumar, Jeetendra
Format: Article
Language:English
Subjects:
quantum communication
quantum computing
quantum computing techniques
quantum cryptography
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Summary:Quantum machine learning (QML) can be employed in solving complicated machine learning tasks although the performance in examining the regression processes is only barely understood. Knowledge gaps are intended to be closed by studying modelling performance of QML in regression tasks, with emphasis being dedicated to scaling up and ability to resist noise. The regression part offers the following functions that include straight line and complex operations. Furthermore, the authors employ quantum neural networks generated using Qiskit to perform experiments. The results demonstrate that QML has a remarkable level of accuracy in basic regressions, reaching a maximum of 97%. Nevertheless, there are difficulties in representing intricate functions, such as 5 × cos(x), which results in a noticeable decline in performance. The work deals with the influence of noise and IERs from imperfect hardware on the efficiency of QML algorithms providing insight into the core obstacles. The result of a detailed examination of the results that have tested the powers and limits of QML in the development of regression applications is represented. The future direction of research and development will be defined by the results obtained in it. Knowledge gaps are intended to be closed by studying the modelling performance of QML in regression tasks, with emphasis being dedicated to scaling up and the ability to resist noise. The regression part offers the following functions that include straight lines and complex operations. Furthermore, the authors employ QNNs generated using Qiskit to perform experiments.
ISSN:2632-8925
2632-8925
DOI:10.1049/qtc2.12100