Thermodynamic Computers Go With the (Energy) Flow

Ada Zejun Shen/ Quanta Magazine

沉泽君/广达杂志

I n the quest to make computers accurate and reliable, noise is the enemy.

在追求让计算机准确可靠的过程中，噪音是敌人。

The thermal jiggling of atoms is a constant threat to the precision needed for detailed calculations.

原子的热抖动一直威胁着详细计算所需的精度。

Whether we’re dealing with familiar classical devices like the laptops or supercomputers that we use today, or fancy quantum devices that promise us faster computation tomorrow, we don’t want some haphazard heat fluctuation to flip a binary digit from a 1 to a 0, sending a calculation off course.

无论我们使用的是我们今天使用的笔记本电脑或超级计算机等熟悉的经典设备，还是未来能够实现更快计算速度的奇特量子设备，我们都不希望出现一些随意的热波动将二进制数字从 1 翻转为 0，从而使计算偏离轨道。

So computer engineers work hard to make computers immune to noise, by switching bits at energies far above the random ripples of the environment.

因此，计算机工程师努力通过以远高于环境随机波动的能量切换比特，使计算机免受噪声影响。

But what if noise could be made into the computer engineer’s friend?

但如果噪音可以进入计算机工程师的朋友中呢？

What if, instead of trying to make devices that work despite the hubbub of thermal fluctuations that ruffle everything in the universe, we could harness that noise to actually do the computing?

如果我们不是试图制造出在热波动扰乱宇宙中一切的喧嚣中仍能工作的设备，而是可以利用这些噪音来实际进行计算，结果会怎样呢？

That’s the goal of a nascent field called thermodynamic computing.

这就是热力学计算这一新兴领域的目标。

Since the Computing Community Consortium hosted its first conference on thermodynamic computing in 2019, a small community of researchers has been laboring to put it into practice.

自计算社区联盟于 2019 年主办首届热力学计算会议以来，一小群研究人员一直在努力将其付诸实践。

Recently, some of them have simulated thermodynamic computation in standard silicon-based logic circuits, showing that the basic concepts seem to work in principle.

最近，他们中的一些人在标准硅基逻辑电路中模拟了热力学计算，表明基本概念似乎在原理上是有效的。

The approach could produce computers that consume little power and dissipate little heat — a huge advantage, given the power-hungry operation of today’s devices and the increasing struggle to prevent ultra-dense miniaturized circuits from melting down.

这种方法可以生产出能耗低、散热少的计算机——考虑到当今设备的高耗电运行以及防止超密集小型电路熔化的日益困难，这是一个巨大的优势。

Thermodynamic computing would make use of thermodynamic processes, which distribute and dissipate energy and inevitably increase randomness at the microscopic scale.

热力学计算将利用热力学过程，它会分配和耗散能量，并不可避免地增加微观尺度的随机性。

“The field is about designing computers that exploit thermodynamics as a computational resource,” said Patrick Coles, a physicist at the startup Normal Computing in New York.

“这个领域的重点是设计利用热力学作为计算资源的计算机，”纽约初创公司 NormalComputing 的物理学家帕特里克·科尔斯 (Patrick Coles) 说。

If it works, it could transform not only the computing industry but the very way we think about computation itself.

如果它有效，它不仅可以改变计算行业，还可以改变我们思考计算本身的方式。

The second law of thermodynamics tells us that the entropy of a closed system should increase over time; things should become less organized overall.

热力学第二定律告诉我们，封闭系统的熵应该随着时间的推移而增加；整体上事情应该变得不那么有条理。

This means that energy gets dissipated as random thermal fluctuations, which are generally of no use to anyone.

这意味着能量会以随机热波动的形式消散，这通常对任何人都没有用处。

Some processes in nature, however, use those fluctuations to find their way to a more organized state.

然而，自然界中的一些过程利用这些波动来找到更有组织的状态。

“I think thermodynamic computing was developed with the thought that it was piggybacking on the computation that ‘already happens out there’ in the rest of the world but [is] not explicitly labeled as such,” said David Sivak , a statistical physicist at Simon Fraser University in Burnaby, Canada.

加拿大伯纳比西蒙弗雷泽大学的统计物理学家戴维·西瓦克 (David Sivak) 表示：“我认为热力学计算的发展是基于这样的想法：它是利用世界其他地方‘已经发生的’计算，但没有明确标记为这样的计算。”

In thermodynamics, the way a physical system changes over time (its dynamics) can be described as a trajectory through an “energy landscape,” a kind of map of the total energies of different configurations of a system’s components.

在热力学中，物理系统随时间变化的方式（其动力学）可以描述为“能量景观”的轨迹，即系统组件不同配置的总能量图。

In this landscape, valleys are where the system settles into comfortable, low-energy configurations, while peaks are high-energy configurations that are relatively unstable.

在这种情况下，山谷是系统进入舒适、低能量配置的地方，而峰值是相对不稳定的高能​​量配置。

A system like this reaches equilibrium when it settles in the lowest valley and stays there.

当这样的系统稳定在最低的山谷并停留在那里时，它就达到平衡。

If this all seems a bit abstract, think about milk.

如果这一切看起来有点抽象，请想想牛奶。

What allows many people to digest dairy products is a chainlike enzyme called lactase, which is produced in the small intestine and fits together like a puzzle piece with the complex sugar in milk called lactose.

使许多人消化乳制品的是一种称为乳糖酶的链状酶，它在小肠中产生，并像拼图一样与牛奶中称为乳糖的复合糖拼合在一起。

The compatibility between their shapes allows lactase to break lactose apart into its simple sugar components, which are more easily managed by the digestive system.

它们形状之间的兼容性使乳糖酶能够将乳糖分解成简单的糖成分，这些成分更容易被消化系统管理。

Processors called thermodynamic sampling units, produced by the startup Extropic, are designed to run generative AI algorithms.

由初创公司 Extropic 生产的称为热力学采样单元的处理器，旨在运行生成式人工智能算法。

But how does lactase get this useful shape?

但乳糖酶是如何获得这种有用的形状的呢？

It is encoded into the sequence of amino acids that make it up.

它被编码成组成它的氨基酸序列。

As the chain is synthesized in the cell, thermal fluctuations allow the chain to explore its energy landscape and crumple up into its most stable equilibrium state.

当链条在细胞中合成时，热波动使链条能够探索其能量景观并崩溃至最稳定的平衡状态。

This process is a kind of thermodynamic computing, in that it solves the problem of folding the lactase into the correct shape using only the thermal energy in the ambient environment of the cell.

这个过程是一种热力学计算，因为它解决了仅利用细胞周围环境中的热能将乳糖酶折叠成正确形状的问题。

Once the chain of amino acids is properly folded, continuing thermal fluctuations merely induce a bit of random wiggling.

一旦氨基酸链正确折叠，持续的热波动只会引起一些随机摆动。

The newly formed enzyme keeps its shape as it sits stably in a deep energy well.

新形成的酶保持其形状，因为它稳定地位于深层能量井中。

Thus, although the operation of a natural system like a living cell proceeds in a milieu pervaded by thermal noise, it still works, often with remarkable energy efficiency.

因此，尽管像活细胞这样的自然系统的运行是在充满热噪声的环境中进行的，但它仍然可以工作，并且通常具有显着的能源效率。

One type of thermodynamic computing, called equilibrium thermodynamic computing, would work like a folding protein.

一种类型的热力学计算称为平衡热力学计算，其工作原理类似于折叠蛋白质。

If you encode the problem you want solved in a system, thermodynamics can drive the system through its energy landscape toward the energy minimum that corresponds to the problem’s optimal solution.

如果您将想要在系统中解决的问题编码，热力学可以驱动系统通过其能量景观走向与问题的最佳解决方案相对应的能量最小值。

“You could take a small electrical circuit, let it evolve naturally under its thermally driven dynamics, and then measure its properties once it has attained thermodynamic equilibrium,” said Stephen Whitelam , a statistical physicist at Lawrence Berkeley National Laboratory in California.

加利福尼亚州劳伦斯伯克利国家实验室的统计物理学家斯蒂芬·怀特拉姆（Stephen Whitelam）表示：“你可以采用一个小型电路，让它在热驱动动力学下自然演化，然后在达到热力学平衡后测量其特性。”

Patrick Coles is the chief scientist of Normal Computing.

帕特里克·科尔斯（Patrick Coles）是普通计算公司的首席科学家。

A different approach to thermodynamic computing takes place when a system is driven away from equilibrium by some source of energy, much as the sun’s heat prevents the Earth’s atmosphere from settling into some unchanging state.

当系统被某种能量来源驱离平衡时，就会出现一种不同的热力学计算方法，就像太阳的热量阻止地球大气层进入某种不变的状态一样。

In this case, the computation takes place as the system moves constantly through its energy landscape.

在这种情况下，计算是在系统在其能量景观中不断移动时进行的。

The trajectory itself encodes the calculation.

轨迹本身对计算进行编码。

These “out-of-equilibrium” processes are ubiquitous in nature; life itself is one of them, powered by a constant flow of energy and matter.

这些“不平衡”过程在自然界中普遍存在。生命本身就是其中之一，由不断流动的能量和物质提供动力。

The trajectories of objects moving under such nonequilibrium conditions display a version of so-called Langevin dynamics , named after the early-20th-century French physicist Paul Langevin.

在这种非平衡条件下运动的物体的轨迹显示出所谓的朗之万动力学的一个版本，该动力学以 20 世纪初法国物理学家保罗·朗之万的名字命名。

Langevin dynamics represents a kind of relaxation: The system seeks to lower its energy, and it dissipates energy in the process.

朗之万动力学代表了一种放松：系统试图降低其能量，并在此过程中耗散能量。

But the system can never settle into a fixed equilibrium state because random impulses, such as thermal fluctuations, constantly push it onto some new course.

但系统永远无法进入固定的平衡状态，因为随机脉冲（例如热波动）不断地将其推向新的方向。

In principle Langevin dynamics can be embodied in an electrical circuit, if it is operating at a power level low enough to be affected by random thermal fluctuations, Whitelam said.

怀特拉姆说，原则上，朗之万动力学可以体现在电路中，前提是电路的运行功率水平足够低，可以受到随机热波动的影响。

If scientists can arrange for the movement of energy through the circuit to correspond to the answer to a calculation, they can use it to do thermodynamic computing.

如果科学家可以安排能量通过电路的运动来对应计算的答案，他们就可以用它来进行热力学计算。

Both equilibrium and nonequilibrium thermodynamic computers receive continuous boosts of energy from thermal fluctuations, Whitelam explained.

怀特拉姆解释说，平衡和非平衡热力学计算机都会从热波动中获得持续的能量提升。

But an equilibrium computer must reach equilibrium to complete its calculation, while a nonequilibrium computer can be designed to complete its calculation on a set timescale.

但是平衡计算机必须达到平衡才能完成其计算，而非平衡计算机可以设计为在设定的时间尺度上完成其计算。

That’s why nonequilibrium approaches to thermodynamic computing are popular, Coles said: They “can potentially be faster, because you’re not waiting for natural equilibration.”

这就是为什么非平衡热力学计算方法很受欢迎，科尔斯说：它们“可能会更快，因为你不需要等待自然平衡。”

Normal Computing’s prototype thermodynamic computers are made of a small array of identical electronic circuits linked together into a network.

普通计算公司的热力学计算机原型由一小组相同的电子电路组成，这些电子电路连接在一起形成一个网络。

Most efforts so far to put thermodynamic computing into practice use silicon-based circuits as analogues.

迄今为止，大多数将热力学计算付诸实践的努力都使用硅基电路作为模拟。

Last year, Coles and colleagues at Normal Computing showed that such a circuit could perform various kinds of computation, including matrix inversion, a mathematical problem that has applications in diverse fields ranging from machine learning to computer graphics, engineering, and finance.

去年，科尔斯和普通计算公司的同事表明，这样的电路可以执行各种计算，包括矩阵求逆，这是一个在机器学习、计算机图形、工程和金融等多个领域都有应用的数学问题。

Normal was founded in 2022 by Faris Sbahi, Antonio Martinez, and Matthias Tan, former members of Google X and Google Brain.

Normal 由 Google X 和 Google Brain 前成员 Faris Sbahi、Antonio Martinez 和 Matthias Tan 于 2022 年创立。

The thermodynamic computer they unveiled was a tailor-made printed circuit board containing eight clusters of components, with each cluster connected to all the others to form a network.

他们推出的热力学计算机是一块定制的印刷电路板，包含八个组件簇，每个簇与其他所有簇连接形成一个网络。

Each cluster contained a simple RLC resonator — a resistor, capacitor, and inductor, a combination that creates an oscillating electrical signal at a particular frequency.

每个簇都包含一个简单的 RLC 谐振器——电阻器、电容器和电感器，这些组合可在特定频率下产生振荡电信号。

The circuit is fed a random electrical signal: Basically, it is driven by noise.

该电路被馈送一个随机电信号：基本上，它是由噪声驱动的。

The strength of the coupling between each pair of RLC resonators in the network can be varied, and all the couplings can be written in the mathematical form of a matrix.

网络中每对 RLC 谐振器之间的耦合强度可以变化，并且所有耦合都可以写成矩阵的数学形式。

It’s basically an electrical version of a set of interconnected springs.

它基本上是一组互连弹簧的电气版本。

The question is: If you give it a shake, how will the dynamics of the network evolve?

问题是：如果你摇一摇，网络的动态将如何演变？

It turns out that, if the network is “shaken” by noise comparable to the energy of the couplings, the equilibrium fluctuations it undergoes correspond to the mathematical inverse of the coupling matrix.

事实证明，如果网络被与耦合能量相当的噪声“震动”，那么它所经历的平衡波动对应于耦合矩阵的数学逆。

“So you can build your device and come back sometime later and measure its fluctuations, and you’ve done matrix inversion,” Whitelam said.

“所以你可以构建你的设备并稍后返回并测量其波动，并且你已经完成了矩阵求逆，”怀特拉姆说。

The circuit made by Coles and colleagues didn’t actually run on ambient noise, which was too low-level to affect the dynamics.

Coles 和同事制作的电路实际上并没有在环境噪声下运行，环境噪声水平太低，不会影响动态。

The researchers had to add in extra noise by hand, using a random-number generator, which cost energy.

研究人员必须使用随机数生成器手动添加额外的噪声，这会消耗能量。

That’s one reason this kind of analog model doesn’t itself demonstrate the promised energy advantages of thermodynamic computing.

这就是这种模拟模型本身无法证明热力学计算所承诺的能源优势的原因之一。

But ultimately, Coles said, the advantage comes from the fact that the computation itself runs “for free” once driven by noise.

但最终，科尔斯表示，优势来自于这样一个事实：一旦受到噪声驱动，计算本身就会“免费”运行。

The Normal team showed that, if they scaled up a processing unit by adding more nodes to its network, it would eventually reach a point at which it could solve problems faster than a regular digital neural network, using considerably less energy and dissipating considerably less heat.

Normal 团队表明，如果他们通过在网络中添加更多节点来扩大处理单元的规模，最终将达到比常规数字神经网络更快地解决问题的程度，并且使用的能源和散发的热量要少得多。

Since unveiling this prototype, Normal has announced a new thermodynamic computer called CN101 that uses digital processing on a silicon chip.

自从推出该原型机以来，Normal 又推出了一款名为 CN101 的新型热力学计算机，该计算机在硅芯片上进行数字处理。

The researchers say that this digital silicon technology is easier to scale up than their analog circuit and can also carry out other kinds of computation, such as image generation and simulations of molecules.

研究人员表示，这种数字硅技术比模拟电路更容易扩展，并且还可以进行其他类型的计算，例如图像生成和分子模拟。

The device has yet to be assessed by other experts.

该设备尚未得到其他专家的评估。

The initial proof of principle by the Normal team served as an inspiration for Whitelam, who recently reported a simulation of a nonequilibrium thermodynamic computing circuit .

Normal 团队的最初原理证明给 Whitelam 带来了灵感，他最近报告了非平衡热力学计算电路的模拟。

Whitelam used a theoretical model of a “denoising” problem, simulated on a classical computer.

怀特拉姆使用了“去噪”问题的理论模型，在经典计算机上进行了模拟。

He trained an algorithm on a video of Paul Langevin’s face being steadily corrupted by noise, dissolving into random static.

他根据保罗·朗之万 (Paul Langevin) 的脸部视频训练了一种算法，视频中的噪音不断破坏，并溶解为随机静电。

(As with the Normal Computing work, the noise was introduced artificially by a random-number generator.) He then demonstrated that the algorithm could start with the static and reconstruct Langevin’s face.

（与正常计算工作一样，噪声是由随机数生成器人为引入的。）然后他证明该算法可以从静态开始并重建朗之万的脸部。

The thermodynamic computer Whitelam used was a network of connected nodes, and its state was determined by how strongly connected those nodes were.

怀特拉姆使用的热力学计算机是一个由连接节点组成的网络，其状态取决于这些节点的连接强度。

During the training process, the algorithm adjusted those connections a little at a time to find the configuration most likely to make Langevin appear.

在训练过程中，算法一次稍微调整这些连接，以找到最有可能使 Langevin 出现的配置。

Stephen Whitelam is a statistical physicist at Lawrence Berkeley National Laboratory.

斯蒂芬·怀特拉姆 (Stephen Whitelam) 是劳伦斯伯克利国家实验室的统计物理学家。

It’s like a network of linked springs, where those springs have different (and adjustable) spring constants.

它就像一个连接弹簧的网络，其中这些弹簧具有不同的（且可调节的）弹簧常数。

Indeed, “you could build a thermodynamic computer from real springs,” Whitelam said — though that would be a curiosity rather than a practical technology.

事实上，“你可以用真正的弹簧建造一台热力学计算机，”怀特拉姆说——尽管这只是一种好奇，而不是一项实用技术。

Whitelam showed that the resulting dynamics followed a path that dissipated the minimal amount of heat — by a rough comparison, about 100 billion times less than would be produced if the same task were performed by a digital neural network.

Whitelam 表明，由此产生的动力学遵循一条散发最少热量的路径——粗略比较一下，如果由数字神经网络执行相同的任务，产生的热量大约要少 1000 亿倍。

The way Whitelam trained his algorithm is a little like the way people currently train algorithms for generative AI — and indeed this may be one of the major applications of thermodynamic computing.

Whitelam 训练算法的方式有点像人们目前训练生成人工智能算法的方式——事实上，这可能是热力学计算的主要应用之一。

“My algorithm is generative in two ways,” Whitelam said.

“我的算法以两种方式生成，”怀特拉姆说。

“First, it turns noise into structure, thereby generating order from disorder.

“首先，它将噪音转化为结构，从而从无序中产生秩序。

Second, if you train it on a set of images, then it can generate additional images that it hasn’t seen before.”

其次，如果你在一组图像上训练它，那么它可以生成以前从未见过的其他图像。”

Normal Computing is not the only startup betting that thermodynamics will feature in the future of computing.

普通计算并不是唯一一家押注热力学将在未来计算中占据重要地位的初创公司。

The Boston-based company Extropic was also founded in 2022 by a team from Google, IBM, Apple, and Microsoft.

总部位于波士顿的 Extropic 公司也是由来自谷歌、IBM、苹果和微软的团队于 2022 年创立。

In October 2025 the company announced “ the world’s first scalable probabilistic computer ” — a grid of thousands of interconnected semiconductor-based components on a chip — which it says can run generative AI algorithms using about 10,000 times less energy than existing algorithms.

2025 年 10 月，该公司宣布推出“世界上第一台可扩展的概率计算机”——一个芯片上由数千个互连的半导体组件组成的网格——据称，该计算机可以运行生成式 AI 算法，其能耗比现有算法少约 10,000 倍。

(The work was recently published in the journal npj Unconventional Computing .)

（这项工作最近发表在《npj UnconventionalComputing》杂志上。）

In addition to potentially offering low-cost, low-dissipation computing, thermodynamic computing might also offer insights into the way natural complex systems work.

除了可能提供低成本、低耗散计算之外，热力学计算还可能提供对自然复杂系统工作方式的深入了解。

Much of what goes on in molecular biology is already described as a sort of information processing.

分子生物学中发生的大部分事情已经被描述为一种信息处理。

For example, a signaling molecule (like a hormone) might arrive at the surface of a cell and have its signal “transduced” — transmitted along a chain of interacting molecules — so that ultimately it flips a switch in the cell nucleus and activates a particular gene.

例如，信号分子（如激素）可能到达细胞表面并“转导”其信号（沿着相互作用的分子链传递），最终翻转细胞核中的开关并激活特定基因。

Cells seem able to conduct this kind of computation very efficiently, creating little energy dissipation and relying, in part, on the intrinsic thermodynamics of intermolecular interactions.

细胞似乎能够非常有效地进行这种计算，几乎不产生能量耗散，并且部分依赖于分子间相互作用的内在热力学。

So are cells themselves a kind of thermodynamic computer?

那么细胞本身是一种热力学计算机吗？

“To my physicist’s way of thinking, it would be fair to say that nature uses Langevin computers programmed by evolution,” Whitelam said.

“按照我的物理学家的思维方式，可以公平地说，大自然使用由进化编程的朗之万计算机，”怀特拉姆说。

The central idea of harnessing thermodynamic fluctuations instead of suppressing them “is indeed thought-provoking,” said Kunihiko Kaneko , a complex-systems theorist at the Niels Bohr Institute in Copenhagen.

哥本哈根尼尔斯·玻尔研究所的复杂系统理论家金子邦彦 (Kunihiko Kaneko) 表示，利用热力学波动而不是抑制热力学波动的中心思想“确实发人深省”。

“But whether this effectively translates to computing in a biological context remains an open question.”

“但这是否能有效地转化为生物学背景下的计算仍然是一个悬而未决的问题。”

The field of thermodynamic computing is in its early days, “analogous to when small-scale quantum computers were built in the 1990s,” as Coles and his colleagues at Normal wrote in their 2025 paper.

正如科尔斯和他在师范大学的同事在 2025 年发表的论文中所写，热力学计算领域正处于早期发展阶段，“类似于 20 世纪 90 年代建造小型量子计算机的时候”。

Quantum computing is now a global industry estimated to be worth around $12 billion.

量子计算现已成为一个价值约 120 亿美元的全球产业。

But it’s a lot easier to build circuits from simple semiconductor-based resonators than from quantum bits that have to be kept in delicate entangled quantum states and often need cryogenic cooling.

但用简单的半导体谐振器构建电路比用量子比特构建电路要容易得多，因为量子比特必须保持在微妙的纠缠量子态，并且通常需要低温冷却。

“The lack of technological barriers for thermodynamic computing can potentially make it a more near-term alternative to quantum computing,” the Normal team wrote.

“热力学计算缺乏技术障碍，有可能使其成为量子计算的近期替代品，”师范大学团队写道。

Whitelam makes a similar comparison with the neural networks that underpin today’s AI.

怀特拉姆对支撑当今人工智能的神经网络进行了类似的比较。

“The computer designs we’ve come up with so far [for thermodynamic computing] are only as capable as the small digital neural networks of around 1990,” he said.

“到目前为止，我们（用于热力学计算）提出的计算机设计只能与 1990 年左右的小型数字神经网络一样强大，”他说。

The history of AI suggests that it should be possible to do better with larger circuits and more training, he said, “but that remains to be seen.” If it pays off, thermodynamic computing will get noisy in more ways than one.

他说，人工智能的历史表明，通过更大的电路和更多的训练应该可以做得更好，“但这还有待观察。”如果它得到回报，热力学计算将以多种方式变得嘈杂。

Correction: July 16, 2026 The Extropic paper describing their scalable probabilistic computer was published in early July 2026.

更正：2026 年 7 月 16 日 描述其可扩展概率计算机的 Extropic 论文于 2026 年 7 月上旬发表。

We have updated the reference.

我们已经更新了参考。

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