Resource saving via ensemble techniques for quantum neural networks
Pre-print
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. https://doi.org/10.48550/arXiv.2303.11283
Type | Pre-print |
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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 conduct 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. |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Middlesex University Theme | Creativity, Culture & Enterprise |
Research Group | Artificial Intelligence group |
Preprint server/collection | arXiv |
Publication dates | |
Online | 20 Mar 2023 |
Publication process dates | |
Deposited | 24 May 2023 |
Accepted | 20 Mar 2023 |
Output status | Published |
Digital Object Identifier (DOI) | https://doi.org/10.48550/arXiv.2303.11283 |
Related Output | |
Is previous version of | Resource saving via ensemble techniques for quantum neural networks |
Language | English |
Publisher | arxiv.org |
File | File Access Level Restricted |
https://repository.mdx.ac.uk/item/8q612
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