Thermodynamic Computing
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Abstract (arXiv)
The hardware and software foundations laid in the first half of the 20th Century enabled the computing technologies that have transformed the world, but these foundations are now under siege. The current computing paradigm, which is the foundation of much of the current standards of living that we now enjoy, faces fundamental limitations that are evident from several perspectives. In terms of hardware, devices have become so small that we are struggling to eliminate the effects of thermodynamic fluctuations, which are unavoidable at the nanometer scale. In terms of software, our ability to imagine and program effective computational abstractions and implementations are clearly challenged in complex domains. In terms of systems, currently five percent of the power generated in the US is used to run computing systems - this astonishing figure is neither ecologically sustainable nor economically scalable. Economically, the cost of building next-generation semiconductor fabrication plants has soared past 10 billion. All of these difficulties - device scaling, software complexity, adaptability, energy consumption, and fabrication economics - indicate that the current computing paradigm has matured and that continued improvements along this path will be limited. If technological progress is to continue and corresponding social and economic benefits are to continue to accrue, computing must become much more capable, energy efficient, and affordable. We propose that progress in computing can continue under a united, physically grounded, computational paradigm centered on thermodynamics. Herein we propose a research agenda to extend these thermodynamic foundations into complex, non-equilibrium, self-organizing systems and apply them holistically to future computing systems that will harness nature's innate computational capacity. We call this type of computing "Thermodynamic Computing" or TC.
Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Nov 2019.
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Citing papers per year: 2022: 1; 2023: 4; 2024: 3; 2025: 3; 2026: 2.
13 papers.
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- 2023Generative learning for nonlinear dynamicscited by 22
- 2023Thermodynamic Computing System for AI Applicationscited by 16
- 2023Thermodynamic AI and the fluctuation frontiercited by 15
- 2025An efficient probabilistic hardware architecture for diffusion-like modelscited by 8
- 2023Thermodynamic Computing via Autonomous Quantum Thermal Machinescited by 5
- 2024Thermodynamic Natural Gradient Descentcited by 4
- 2024Nonlinear thermodynamic computing out of equilibriumcited by 4
- 2025Training thermodynamic computers by gradient descentcited by 4
- 2024Thermodynamic Bayesian Inferencecited by 2
- 2026Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisationcited by 1
- 2022Fault-Tolerant Neural Networks from Biological Error Correction Codescited by 0
- 2025Thermodynamics-Inspired Computing with Oscillatory Neural Networks for Inverse Matrix Computationcited by 0
1 more
- 2026Thermalizing Stochastic Programscited by 0
- 2026Thermalizing Stochastic Programs3 Aug 2026
- 2026Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation17 Feb 2026
- 2025An efficient probabilistic hardware architecture for diffusion-like models28 Oct 2025
- 2025Training thermodynamic computers by gradient descent18 Sep 2025
- 2025Thermodynamics-Inspired Computing with Oscillatory Neural Networks for Inverse Matrix Computation30 Jul 2025
- 2024Nonlinear thermodynamic computing out of equilibrium22 Dec 2024
- 2024Thermodynamic Bayesian Inference2 Oct 2024
- 2024Thermodynamic Natural Gradient Descent22 May 2024
- 2023Thermodynamic Computing System for AI Applications8 Dec 2023
- 2023Generative learning for nonlinear dynamics7 Nov 2023
- 2023Thermodynamic Computing via Autonomous Quantum Thermal Machines30 Aug 2023
- 2023Thermodynamic AI and the fluctuation frontier9 Feb 2023
1 more
- 2023Generative learning for nonlinear dynamicscited by 22
- 2023Thermodynamic Computing System for AI Applicationscited by 16
- 2023Thermodynamic AI and the fluctuation frontiercited by 15
- 2025An efficient probabilistic hardware architecture for diffusion-like modelscited by 8
- 2023Thermodynamic Computing via Autonomous Quantum Thermal Machinescited by 5
- 2024Thermodynamic Natural Gradient Descentcited by 4
- 2024Nonlinear thermodynamic computing out of equilibriumcited by 4
- 2025Training thermodynamic computers by gradient descentcited by 4
- 2024Thermodynamic Bayesian Inferencecited by 2
- 2026Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisationcited by 1
- 2022Fault-Tolerant Neural Networks from Biological Error Correction Codescited by 0
- 2025Thermodynamics-Inspired Computing with Oscillatory Neural Networks for Inverse Matrix Computationcited by 0
12 most cited of 13 All 13 (JSON)
- 2026Thermalizing Stochastic Programs3 Aug 2026
- 2026Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation17 Feb 2026
- 2025An efficient probabilistic hardware architecture for diffusion-like models28 Oct 2025
- 2025Training thermodynamic computers by gradient descent18 Sep 2025
- 2025Thermodynamics-Inspired Computing with Oscillatory Neural Networks for Inverse Matrix Computation30 Jul 2025
- 2024Nonlinear thermodynamic computing out of equilibrium22 Dec 2024
- 2024Thermodynamic Bayesian Inference2 Oct 2024
- 2024Thermodynamic Natural Gradient Descent22 May 2024
- 2023Thermodynamic Computing System for AI Applications8 Dec 2023
- 2023Generative learning for nonlinear dynamics7 Nov 2023
- 2023Thermodynamic Computing via Autonomous Quantum Thermal Machines30 Aug 2023
- 2023Thermodynamic AI and the fluctuation frontier9 Feb 2023
12 newest of 13 All 13 (JSON)