熱力学コンピューティングとは？熱雑音を計算に使う仕組み

"But thermodynamic computing, a branch of unconventional computing, inverts the paradigms of both and uses those same fluctuations as its power source. This drastically reduces the amount of external energy required to perform computations and allows for operation at room temperature, unlike many quantum computers."／"Modern computing requires energy: a single Google search, for example, consumes enough energy to power a six-watt LED for three minutes."／"In classical computers, even the smallest devices, such as transistors and gates, operate at energy scales thousands of times larger than that of this vibration."／"This difference in scale between signal and noise enables the consistent output that makes computation possible, but it comes at an energy cost: classical computers require large amounts of power to work reliably and operate far above the threshold of thermodynamic efficiency."／"The premise of thermodynamic computing is that if you take a physical device with an energy scale comparable to that of thermal energy and leave it alone, it will change state over time, driven by thermal fluctuations. The goal is to program it so that this time evolution does something useful. Classical and quantum computing fight noise; thermodynamic computing is powered by it."（1つ目は記者の地の文／2つ目〜4つ目も記者の地の文／5つ目は Stephen Whitelam のコメント）— Berkeley Lab News Center より

"First, existing thermodynamic computers are designed to do computation at thermodynamic equilibrium, meaning researchers must wait for the computer to settle into its lowest-energy configuration before they can perform a calculation. Even if a system’s ground state is well-defined, the amount of time it takes to reach equilibrium is unpredictable – and it can be too long to be practical for day-to-day computational use."／"Additionally, the range of computations that can be performed using thermodynamic computing has been limited to solving linear algebra problems."（いずれも Overcoming roadblocks の節）— Berkeley Lab News Center より

"when the components of the computer are themselves nonlinear, it becomes possible to train a thermodynamic computer to perform nonlinear computations at specified times, regardless of its equilibrium status. This means the computer operates more like a classical computer, without the need to wait for equilibrium."／"A nonlinear thermodynamic circuit can behave like a neuron in a neural network,"／"Nonlinearity is what gives a neural network its expressive power. What we reasoned is that if you build these thermodynamic neurons into a connected structure, then that structure should have the expressive power to mimic a neural network and so be able to do machine learning."（1つ目は記者の地の文／2つ目・3つ目は Stephen Whitelam のコメント）— Berkeley Lab News Center より

"We simulate a digital model of a thermodynamic neural network, and show that its parameters can be adjusted by genetic algorithm to perform nonlinear calculations at specified observation times, regardless of whether the system has attained thermal equilibrium."（Abstract）— Nature Communications, vol. 17, Article 1189 (2026) より

"A thermodynamic computer is a stochastic system, meaning that no two runs on a thermodynamic computer look the same, and the methods used for training digital neural networks don’t apply."／"In particular, he used a framework known as a genetic algorithm: beginning with a set of different thermodynamic neural networks and evaluating the effectiveness of each, he selected the best-performing and mutated them, adding random noise to their parameters, and evaluated them again."／"Using 96 GPUs in parallel on the Perlmutter supercomputer at NERSC, Casert built and ran massively parallel evolutionary simulations, evaluating billions of noisy dynamical trajectories per generation to discover the most effective network parameters."／"Ultimately, Casert simulated more than a trillion runs of a thermodynamic computer, using Perlmutter’s GPUs in parallel."（いずれも Inverted training の節）— Berkeley Lab News Center より

"According to Whitelam, it’s important to work out how to realize these designs in hardware."／"Existing algorithms are meant for systems working at equilibrium; with that requirement no longer a roadblock, new ones will need to be developed."／"Currently, the team is looking for experimental partners to make both hardware and software a reality — another step exploring what’s possible with thermodynamic computing."／"This training framework is considerably more costly than the methods used to train digital networks, but it yields a computer that can operate using very little energy after it’s built and trained."／"Training a thermodynamic neural network by simulating it digitally is expensive, but once trained and built as physical hardware, we can perform inference on that hardware for a very low energy cost,"／"the researchers have proposed a design and training framework for a type of thermodynamic computer that mimics a neural network, which could drastically reduce the energy requirements of machine learning."／"This drastically reduces the amount of external energy required to perform computations and allows for operation at room temperature, unlike many quantum computers."（1〜3つ目は More hardware, more algorithms の節／4つ目は Inverted training の節／5つ目は Casert のコメント／6つ目は冒頭の地の文／7つ目は Introduction 相当の地の文）— Berkeley Lab News Center より

"We are building an entirely new type of computing hardware that is designed from the ground up to be incredibly energy efficient at running generative AI models."（Thermodynamic Hardware の節）— Extropic 公式サイトより