非线性热力学计算失去了平衡
Stephen Whitelam1, Corneel Casert2,3
1Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. swhitelam@lbl.gov.
Nature communications
|January 10, 2026
概括
本研究介绍了一台使用热波动进行非线性计算的热力学计算机. 这些热力学神经网络可以近似函数,甚至在失衡的情况下运行.
科学领域:
- 物理 物理学 物理
- 计算机科学 计算机科学
- 热力学是一种热力学.
背景情况:
- 经典的神经网络执行复杂的计算,但需要大量的能量.
- 热力学计算提供了一个潜在的低能耗替代方案,传统上专注于平衡系统.
研究的目的:
- 设计一种能够任意进行非线性计算的热力学计算机.
- 探索热力学系统在平衡状态下运行的潜力,用于计算.
- 开发热力学神经网络作为通用函数近似器.
主要方法:
- 设计具有波动自由度的简单热力学电路.
- 将这些电路限制在四极电位内,并将它们合到热浴中.
- 模拟一个热力学神经网络的数字模型.
- 使用遗传算法调整网络参数.
主要成果:
- 热力学电路作为输入的非线性函数表现出活动,作为热力学神经元.
- 网络电路形成热力学神经网络,能够实现通用函数近似.
- 模拟表明在指定时间成功进行非线性计算,不管平衡状态如何.
结论:
- 热力学计算可以扩展到平衡条件之外.
- 这种方法可以实现与经典神经网络类似的完全非线性计算.
- 开发的热力学神经网络由热波动提供动力.
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