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English(EN) Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks

Qiskit 机器学习库遭受数据丢失,影响量子神经网络

最近发表在 arXiv 上的一篇论文详细介绍了一个 IBM Qiskit 机器学习库中的严重数据丢失问题,特别是影响了 SamplerQNN 类。此问题发生的原因是为模拟器设计的后处理例程对量子比特空间做出了假设,而这些假设在更大的量子硬件上不成立。该问题导致有效测量次数大幅减少,扭曲了预测和损失值,并影响了模型训练和推理的准确性。一个修复方案已被实施并合并到 GitHub 代码库中。 AI

影响 通过解决后处理中的数据丢失问题,有望提高量子神经网络的准确性和训练敏感性。

排序理由 学术论文,详细介绍了开源库中的一个技术问题及其修复。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Qiskit 机器学习库遭受数据丢失,影响量子神经网络

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学术论文,详细介绍了开源库中的一个技术问题及其修复。 [lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Soraya V. Panambalom, Edoardo Altamura, Nick Chancellor, Jonte R. Hance ·

    后处理中数据丢失对量子神经网络训练和推理的影响

    arXiv:2609.05060v1 Announce Type: cross Abstract: As quantum hardware scales to larger devices, the classical software layers that interface with it must evolve in step. Postprocessing routines developed and tested primarily in simulator settings can encode assumptions that no lo…