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English(EN) Sustained Performance and Energy Accounting for Nonlinear Forecasting Across Classical and Simulated Quantum Models

量子和经典模型在节能预测方面的比较

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文探讨了非线性预测模型的性能和能源效率，将经典方法与模拟量子模型进行了比较。该研究评估了跨多个数据集的各种配置，测量了准确性、速度、内存使用和能源消耗。虽然具有 Transformer 读出的连续变量量子储层计算 (QRC) 实现了最佳预测准确性，但传统的回声状态网络和岭滞模型展示了卓越的持续能源效率。 AI

影响 强调了 AI 模型在预测准确性和能源效率之间的权衡，为可持续 AI 开发提出了新的核算方法。

排序理由 研究论文，详细介绍了 AI 模型的比较性能和能源效率。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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量子和经典模型在节能预测方面的比较

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研究论文，详细介绍了 AI 模型的比较性能和能源效率。[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) · Avyay Kodali, Priyanshi Singh, Pranay Pandey, Krishna Bhatia, Shalini Devendrababu, Srinjoy Ganguly ·

    经典和模拟量子模型中非线性预测的持续性能和能耗核算

    arXiv:2510.25183v2 Announce Type: replace-cross Abstract: Energy-efficient AI should be evaluated across the full application pipeline, not only by lowest error or shortest training time. We study this through nonlinear time-series forecasting using simulated quantum reservoir co…

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