Fascination Technology A Computer Without Circuits—Built from Liquid and Particles

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In our "Fascination Technology" section, we present impressive projects from research and development to designers every week. Today: a computer that operates entirely without electronic circuits—instead, oscillating micro-particles in a liquid take over the computational work.

A computer without a single transistor, without a circuit, without a chip—instead, just a liquid in which several hundred microscopic particles circulate. What sounds like science fiction has now been experimentally realized by researchers from the Universities of Konstanz (Germany) and Stuttgart (Germany) under Clemens Bechinger. Their system utilizes a principle called "reservoir computing"—implemented for the first time in a microscopic multi-particle system.
 

A computer does not necessarily have to consist of electronic circuits: The collective movement of microscopic oscillators opens up a new form of information processing.

The Principle: A Pond as a Calculating Unit

You can think of the concept as a pond into which a stone is thrown: the waves that arise overlap into a complex but characteristic pattern. Anyone who reads this pattern closely enough can draw conclusions about the stone – without having to calculate the physics of each individual wave in detail.This is exactly what happens in the Konstanz experiment: Several hundred microparticles move in a liquid almost in circular orbits. When data is imprinted on them as an input signal, their movements couple with each other through the liquid, creating a complex but reproducible movement pattern—the "reservoir." This pattern already contains a highly complex processing of the input data, which a classical computer would only be able to generate with significant effort."The actual evaluation then becomes surprisingly simple: You specifically measure certain relatively simple properties of the particle movement and combine them to obtain the desired output," explains first author Veit-Lorenz Heuthe.

Why It Works Without Understanding It

The perhaps most unusual aspect: The researchers do not need to fully comprehend the physical details of the particle motion. "Unlike classical computers, one does not need to understand the underlying dynamics in detail or fully control them," says Bechinger, Professor of Soft Condensed Matter at the University of Konstanz. "What matters is only that the system responds reliably to inputs—then its physics can be directly used for information processing."This differentiates the approach from classical processors and also from artificial neural networks: both require precisely designed, controlled structures. The more complex the task, the greater the effort and energy demand. The liquid system, on the other hand, lets the physics do the work—the evaluation is limited to a few, simple measuring parameters.

The liquid system lets physics do the work—the evaluation is limited to a few simple measurement variables.

What the Approach Can Already Do

In tests, the team succeeded in using the particle system to precisely predict chaotic time series—a task that typically requires substantial computational power from classical algorithms. Additionally, it was demonstrated that even the smallest irregularities in noisy input data can be reliably detected.This is relevant for practical applications: Strongly noisy measurement signals, such as those from seismology or climate research, often contain fine precursor patterns before an event occurs—such as an earthquake or a tipping point in a climate system. A physical reservoir like this could detect such early warning signals directly during the measurement process, without the need for extensive digital data analysis.

Laboratory System with Perspective 

It is still a purely fundamental experiment—far from a functional device. However, the principle demonstrates a path that goes beyond pure physics curiosity: information processing that arises directly from the dynamics of complex physical systems, rather than in specially designed circuits. In the long term, this could lead to energy-efficient computing concepts and intelligent sensors that evaluate data where it originates—for example, directly within the sensor itself, instead of sending it to a processor for evaluation.

Original publication:
Veit-Lorenz Heuthe, Lukas Seemann, Samuel Tovey, and Clemens Bechinger
Reservoir computing from collective dynamics of active colloidal oscillators. Commun. AI Comput. 1, 6 (2026).
DOI: https://doi.org/10.1038/s44488-026-00001-3