English(EN) Performance Evaluation of Ising and QUBO Variable Encodings in Boltzmann Machine Learning

Ising 与 QUBO 编码对玻尔兹曼机学习收敛性的影响

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

一篇新的研究论文评估了 Ising 和 QUBO 变量编码在玻尔兹曼机学习中的性能。研究发现，QUBO 编码可能导致 Fisher 信息矩阵的病态，从而导致随机梯度下降的收敛速度变慢。然而，使用自然梯度下降或适当的预处理技术可以缓解这些问题，从而在两种编码类型之间实现相似的收敛速度。这些发现为选择玻尔兹曼机的变量编码和预处理方法提供了实用的指导。 AI

影响通过变量编码选择为优化玻尔兹曼机学习性能提供实用指南。

排序理由学术论文，详细介绍机器学习编码的研究成果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Yasushi Hasegawa, Masayuki Ohzeki · 2026-10-05 04:00

玻尔兹曼机学习中Ising和QUBO变量编码的性能评估

arXiv:2510.13210v2 Announce Type: replace Abstract: We compare Ising ({-1, +1}) and QUBO ({0, 1}) encodings for Boltzmann machine learning under controlled protocols that fix the sampler, optimizer, and learning-rate design within each comparison. Exploiting the identity that the…

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玻尔兹曼机学习中Ising和QUBO变量编码的性能评估

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