Diffusion-inspired quantum noise mitigation in parameterized quantum circuits

doi:10.1007/s42484-025-00276-2

2025-04-16

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DOI：10.1007/s42484-025-00276-2

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参数化量子电路（PQCs）已被公认为利用近期量子优势在多个问题中的领先策略，包括机器学习和组合优化。当应用于特定任务时，量子电路中的参数被训练以最小化目标函数。尽管已有大量研究致力于提升PQCs在实际任务中的性能，但量子噪声引起的误差在运行于真实量子计算机时会降低其性能。特别是，当量子态通过多层量子电路变换时，量子噪声的影响会累积，并逐渐接近最大混合态或完全噪声。本文研究了量子噪声与扩散模型之间的关系。随后，我们提出了一种新颖的受扩散启发的学习方法，以减轻PQCs中的量子噪声并降低特定任务的误差。通过我们的实验，我们展示了该学习策略的有效性，并在量子噪声场景下的分类任务中实现了最先进的表现。

Keyword:

Quantum machine learning

Quantum information

Quantum noise

Diffusion models

期刊

Quantum Machine Intelligence

IF:

4.4

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453

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796

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Department of Physics

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Department of Electrical and Computer Engineering

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Department of Electrical Engineering and Computer Science

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