研究人员开发了一个名为 Quantum Occam Learning 的新框架，以解决量子机器学习模型的表达能力问题。该理论侧重于量子模型在从有限数量的量子状态副本中学习时能够多好地表示数据。该框架建立了一个样本支持的表达定律，表明模型的门数量受限于样本数量和期望的准确度。 AI
影响 基于数据样本建立了量子模型表达能力的理论极限，指导未来的量子机器学习研究。
排序理由 这是一篇详细介绍量子机器学习新理论框架的研究论文。
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arXiv:2606.12211v1 Announce Type: cross Abstract: A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest. Yet, the expressibility is statistically meaningful only insofar as it can be learned from fi…
A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest. Yet, the expressibility is statistically meaningful only insofar as it can be learned from finitely many copies of an unknown quantum state. In…