Potential and limitations of quantum extreme learning machines
Potential and limitations of quantum extreme learning machines
复制标题
量子极限学习机的潜力和局限性
DOI:
10.1038/s42005-023-01233-w
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发表时间:
2023
影响因子:
5.5
通讯作者:
Innocenti L
中科院分区:
文献类型:
--
作者:
Innocenti L
Quantum 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.
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作者:
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通讯作者:
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DOI:
--
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DOI:
--
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2006
期刊:
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通讯作者:
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