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Updated: Sep 19, 2025

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优化温度分布用于训练神经量子状态,使用并行炼
Conor Smith1,2,3, Quinn T Campbell4, Tameem Albash4
1University of New Mexico, Center for Quantum Information and Control, University of New Mexico, Albuquerque, New Mexico 87131, USA.
Physical review. E
|June 19, 2025
概括
在人工神经网络 (ANN) 的并行炼中优化温度分布显著提高了变量算法的成功率. 这种自适应方法有效地克服了参数景观中的局部最小值,并且计算开销最小.
科学领域:
- 量子多体物理学 量子多体物理学
- 机器学习在物理学中的应用
- 计算凝聚物质的计算方法
背景情况:
- 参数化人工神经网络 (ANN) 是研究量子多体系统的强大工具.
- 由于参数格局中的局部最小值,培训ANN可能具有挑战性.
- 并行炼是一种用于减轻训练困难的方法.
研究的目的:
- 为了研究温度分布在ANN培训的平行炼的影响.
- 开发一种可适应的方法来优化复制温度.
- 为了提高使用优化并行炼的变化算法的成功率.
主要方法:
- 采用了自适应温度调整方法,以使平行炼复制品之间的交换概率相等.
- 该方法在两种类型的ANN上进行了测试:受限制的博尔兹曼机器和前网络.
- 在一个玩具模型上进行了模拟,该模型具有变不变的哈密尔顿和J1-J2模型在一个矩形格子上.
主要成果:
- 优化的温度分布显著提高了变量算法的成功率.
- 适应性方法有效地消除了复制随机步行中的瓶.
- 温度优化造成了微不足道的额外计算成本.
结论:
- 在并行炼中进行自适应温度优化是提高ANN在变化算法方面的训练的高效策略.
- 这种方法提供了一种计算成本低廉的方法来提高量子多体模拟的性能.
- 这些发现适用于各种ANN架构和量子哈密尔顿.
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