Potential and limitations of quantum extreme learning machines

Potential and limitations of quantum extreme learning machines
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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
Innocenti L
中科院分区:
物理与天体物理1区
文献类型:
--
作者:
Innocenti L

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量子极限学习机(QELM)的目标是有效地对固定的(通常是未校准的)量子设备的结果进行后处理,以解决诸如估计量子态的性质等任务。目前缺乏对其潜力和局限性的描述,这将使这种方法能够充分部署,以解决系统识别、设备性能优化以及状态或过程重建等问题。我们提出了一个建模QELM的框架,表明它们可以通过单一有效的测量得到简洁的描述,并提供了用这种协议准确检索的信息的明确表征。此外,我们还发现QELM的训练过程与重建表征给定设备的有效测量的过程有密切的相似之处。我们的分析为更深入地理解QELM的能力和局限性铺平了道路,并有可能成为一种对噪声和缺陷更具弹性的量子态估计的强大测量范式。
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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