English(EN) Quantum MeanFlow: single-shot generative sampling on NISQ hardware

Quantum MeanFlow 在量子硬件上实现单步生成采样

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员推出了一种名为 Quantum MeanFlow (QMF) 的新方法，用于在量子计算机上进行单步生成采样。该方法是经典 MeanFlow 的类似物，学习时间间隔内的平均速度场，这与 Quantum Flow Matching (QFM) 学习的瞬时速度场形成对比。QMF 旨在降低与量子硬件上顺序电路提交相关的高输入/输出成本。虽然 QMF 生成的图像质量低于多步 QFM，但在各种采样次数下，其性能优于单步 QFM 采样。该方法使用 IBM 量子计算机在 MNIST 数据集上进行了基准测试，证明了其在高效量子生成采样方面的可行性。 AI

影响引入了一种减少生成采样量子电路评估的方法，有可能加速量子机器学习领域的研究。

排序理由学术论文，详细介绍了一种新的量子生成模型方法。[lever_c_demoted from research: ic=1 ai=1.0]

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AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Ashish Joshi, Eshaan Mistry, Takahiko Koyama · 2026-09-03 04:00

Quantum MeanFlow：在NISQ硬件上进行单次生成采样

arXiv:2609.02186v1 Announce Type: cross Abstract: Quantum generative models offer a promising framework for exploring whether quantum computation can enhance generative machine learning. Flow matching is a generative method in which samples are generated by transporting a simple,…

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Quantum MeanFlow：在NISQ硬件上进行单次生成采样

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