Resource saving via ensemble techniques for quantum neural networks
Article
Incudini, M., Grossi, M., Ceschini, A., Mandarino, A., Panella, M., Vallecorsa, S. and Windridge, D. 2023. Resource saving via ensemble techniques for quantum neural networks. Quantum Machine Intelligence. 5 (2). https://doi.org/10.1007/s42484-023-00126-z
Type | Article |
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Title | Resource saving via ensemble techniques for quantum neural networks |
Authors | Incudini, M., Grossi, M., Ceschini, A., Mandarino, A., Panella, M., Vallecorsa, S. and Windridge, D. |
Abstract | Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limited qubits or hardware noise, conducting large-scale experiments often requires significant resources. Moreover, the output of the model is susceptible to corruption by quantum hardware noise. To address this issue, we propose the use of ensemble techniques, which involve constructing a single machine learning model based on multiple instances of quantum neural networks. In particular, we implement bagging and AdaBoost techniques, with different data loading configurations, and evaluate their performance on both synthetic and real-world classification and regression tasks. To assess the potential performance improvement under different environments, we conducted experiments on both simulated, noiseless software and IBM superconducting-based QPUs, suggesting these techniques can mitigate the quantum hardware noise. Additionally, we quantify the amount of resources saved using these ensemble techniques. Our findings indicate that these methods enable the construction of large, powerful models even on relatively small quantum devices. |
Keywords | Ensemble technique; Bagging; Boosting; Quantum neural network; Quantum machine learning |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Middlesex University Theme | Creativity, Culture & Enterprise |
Publisher | Springer |
Journal | Quantum Machine Intelligence |
ISSN | 2524-4906 |
Electronic | 2524-4914 |
Publication dates | |
Online | 29 Sep 2023 |
Dec 2023 | |
Publication process dates | |
Submitted | 15 Mar 2023 |
Accepted | 11 Aug 2023 |
Deposited | 09 May 2024 |
Output status | Published |
Publisher's version | License File Access Level Open |
Copyright Statement | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s42484-023-00126-z |
Web of Science identifier | WOS:001079309800002 |
Related Output | |
Is new version of | Resource saving via ensemble techniques for quantum neural networks |
Language | English |
https://repository.mdx.ac.uk/item/135zy2
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