تقرير
Resource Saving via Ensemble Techniques for Quantum Neural Networks
| العنوان: | Resource Saving via Ensemble Techniques for Quantum Neural Networks |
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| المؤلفون: | Incudini, Massimiliano, Grossi, Michele, Ceschini, Andrea, Mandarino, Antonio, Panella, Massimo, Vallecorsa, Sofia, Windridge, David |
| المصدر: | Quantum Machine Intelligence, vol. 5, no. 2, pp. 1-24, ISSN: 2524-4906, Springer Nature, Germany, December 2023 |
| سنة النشر: | 2023 |
| المجموعة: | Quantum Physics |
| مصطلحات موضوعية: | Quantum Physics |
| الوصف: | 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. Comment: Extended paper of the work presented at QTML 2022. Close to published version |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/s42484-023-00126-z |
| URL الوصول: | http://arxiv.org/abs/2303.11283 |
| رقم الأكسشن: | edsarx.2303.11283 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/s42484-023-00126-z |
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