Abstract
AbstractQuantum extreme learning machines (QELMs) aim to efficiently post-process the outcome of fixed — generally uncalibrated — quantum devices to solve tasks such as the estimation of the properties of quantum states. The characterisation of their potential and limitations, which is currently lacking, will enable the full deployment of such approaches to problems of system identification, device performance optimization, and state or process reconstruction. We present a framework to model QELMs, showing that they can be concisely described via single effective measurements, and provide an explicit characterisation of the information exactly retrievable with such protocols. We furthermore find a close analogy between the training process of QELMs and that of reconstructing the effective measurement characterising the given device. Our analysis paves the way to a more thorough understanding of the capabilities and limitations of QELMs, and has the potential to become a powerful measurement paradigm for quantum state estimation that is more resilient to noise and imperfections.
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy
Reference41 articles.
1. Huang, G.-B., Zhu, Q.-Y. & Siew, C.-K. 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), Vol. 2, 985-990 (IEEE, 2004).
2. Huang, G.-B., Wang, D. H. & Lan, Y. Extreme learning machines: a survey. Int. J. Machine Learn. Cybern. 2, 107–122 (2011).
3. Wang, J., Lu, S., Wang, S.-H. & Zhang, Y.-D. Multimedia Tools and Applications 1–50, https://link.springer.com/article/10.1007/s11042-021-11007-7 (2021).
4. Lukoševičius, M. & Jaeger, H. Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3, 127–149 (2009).
5. Lukoševičius, M. Neural Networks: Tricks of the Trade. 659–686, https://link.springer.com/chapter/10.1007/978-3-642-35289-8_36 (2012).
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