In the relentless pursuit of next-generation computing devices, one fundamental hurdle remains: fully understanding how these systems consume energy at their most basic levels. Conventional thermodynamic concepts, while powerful in macroscopic settings, falter when applied to the microscopic and quantum regimes. Researchers at Stanford University have now made a groundbreaking leap forward by developing a method that directly measures the intricate energy flows of nanoscale quantum systems operating far from equilibrium. Their pioneering work, recently published in Nature Physics, bridges the gap between theoretical predictions and experimental realities, presenting unprecedented insights into quantum energy dissipation.
The cornerstone of this research lies in studying ultrafine nanocrystals known as quantum dots, whose light emission is governed by quantum mechanical effects unique to their nanoscale dimensions. These quantum dots undergo rapid switching between “on” and “off” states—a blinking pattern that signals dynamic shifts in their internal states. By manipulating these blinking behaviors with external fields, the team induced controlled non-equilibrium conditions. This approach allowed them to probe how information is lost and energy is dissipated in real time during microscopic processes that are typically elusive to direct observation.
A critical concept employed in this study is entropy production, a thermodynamic quantity that quantifies the irreversibility of a process and essentially measures how much information about the system’s microscopic states is lost over time. Until now, measuring entropy production in driven quantum systems was deemed nearly impossible due to technical challenges and inherent noise in experimental setups. Using an innovative combination of precise quantum dot experiments and sophisticated machine learning algorithms, the team optimized parameters in physics-based models, enabling precise calculations of entropy production values with ultrahigh sensitivity.
The implications of quantifying entropy production at the quantum scale reach far beyond academic curiosity. Such measurements delineate fundamental performance boundaries for future devices, revealing how fast computations can be executed and how efficiently energy is utilized. This addresses a pivotal question in the development of tomorrow’s technology: how to design systems that minimize wasted energy while maximizing operational speed and stability—a feat crucial for sustainable and powerful computing architectures.
One of the senior authors, Aaron Lindenberg, emphasized that our natural world is inherently out of thermodynamic equilibrium. This non-equilibrium nature governs everything from climate patterns and biological processes to the operation of materials and devices. However, prior to this work, no one had succeeded in quantifying the essential thermodynamic metric of entropy production in a genuine material system under such driven, non-equilibrium conditions. This accomplishment sets a new benchmark in the study of nonequilibrium statistical mechanics and quantum thermodynamics.
According to Grant Rotskoff, an assistant professor of chemistry and co-author, the achievement is doubly remarkable because experimental techniques have lagged behind theoretical developments in this space. While theories exploring thermodynamics at nanoscale and quantum regimes have flourished, there was a vast experimental divide. This new work significantly narrows that gulf by providing a practical and replicable method to measure efficiency and energy dissipation experimentally in complex, small-scale systems.
The researchers cleverly induced non-equilibrium states in quantum dots by applying external fields, which altered their blinking statistics between distinct patterns. By capturing these statistical shifts, the team was able to correlate fluctuations and transitions with the underlying thermodynamics of energy and information. Such detailed characterization required the interplay of ultra-sensitive instrumentation, high-resolution data acquisition, and modern computational methods to elucidate otherwise hidden physical behavior.
Machine learning played an indispensable role in this research by refining the parameters of the physics-based models that describe the quantum dot systems. This optimization was necessary to counterbalance experimental noise and theoretical idealizations, allowing the researchers to extract meaningful entropy production rates from complex, real-world data. The fusion of data science with quantum physics marked a novel methodological advancement, demonstrating the potential of interdisciplinary approaches for tackling longstanding measurement problems.
Beyond its immediate scientific impact, this study lays the groundwork for future technological innovations. By establishing a reliable framework for quantifying energy dissipation in driven quantum systems, device engineers can explore novel pathways for optimizing performance. This could lead to faster, more energy-efficient computation and memory devices, contributing to the global effort to mitigate the environmental footprint of information technologies.
Yuejun Shen, the lead author and a graduate student at Stanford, noted the difficulty in translating theoretical models into viable experiments. The team’s approach represents a pragmatic middle ground, making the theoretical ideas experimentally accessible without oversimplifying the complexities of real materials. This advancement could catalyze a wave of experimental exploration into the thermodynamics of non-equilibrium quantum phenomena.
The rapid progress in computational capabilities, data analysis techniques, machine learning, and experimental instrumentation—combined with contemporary theoretical understanding—has made such studies feasible today. A decade ago, the precise measurement and modeling described in this paper would have been technically prohibitive, highlighting how scientific frontiers evolve hand-in-hand with technological advances.
Ultimately, the researchers envision their work as a foundational step toward a new class of nanoscale devices that intelligently balance speed, stability, and energy consumption through optimized thermodynamic control. The ability to directly measure and manipulate entropy production within these systems opens exciting possibilities in fields ranging from quantum computing to nanoelectronics and energy harvesting.
Looking forward, the team plans to refine their methodology further, increasing its resolution and applying it to progressively complex material systems. Their interdisciplinary strategy, combining physics, chemistry, engineering, and data science, exemplifies how multifaceted approaches drive breakthrough innovations in understanding and harnessing the subtle interplay of energy, information, and quantum mechanics.
This milestone represents an inspiring confluence of theory, measurement, and computational ingenuity, ultimately illuminating the thermodynamic underpinnings of the microscopic world. As energy constraints become ever more critical in technological development, insights gleaned from this work promise to shape the evolution of sustainable, high-performance quantum devices for decades to come.
Subject of Research: Quantum thermodynamics, energy dissipation, and entropy production in nanoscale materials
Article Title: Quantifying Entropy Production in Driven Quantum Dot Systems via Experimental and Machine Learning Techniques
News Publication Date: February 9, 2026
Web References:
References:
Shen, Y., Ma, H., Saunders, A., Heide, C., Liu, F., Shi, J., Chen, C., Rotskoff, G., & Lindenberg, A. (2026). Measurement of entropy production in a driven quantum dot system. Nature Physics. https://doi.org/10.1038/s41567-026-03177-8
Image Credits: Stanford University School of Engineering
Keywords
Energy, Quantum dots, Nanomaterials, Entropy, Thermodynamics, Non-equilibrium systems, Quantum thermodynamics, Machine learning, Energy efficiency, Nanoscale measurement, Quantum information, Information dissipation
