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A microscopic experiment shows that memory held in an environment can be turned into useful work. Using an optically trapped Brownian particle in a viscoelastic fluid, researchers extracted work from information that returned from otherwise hidden bath degrees of freedom—surpassing the corresponding memoryless, or Markovian, benchmark in one measured state.
What the experiment demonstrated
Lokesh Muruga, Felix Ginot, Sarah A. M. Loos and Clemens Bechinger report the result in an open-access Nature Physics article published on 16 October 2026. Their system is an optically trapped Brownian particle immersed in a viscoelastic fluid. Unlike an ideal memoryless heat bath, a viscoelastic bath retains correlations from earlier interactions, so the particle follows non-Markovian dynamics.
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The central result is that this environmental memory is not merely a complication. A feedback protocol can access part of the information stored in hidden bath degrees of freedom and convert it into extracted work. In one measured state, the maximum work exceeded the optimum expected for the corresponding Markovian system, indicating that energy associated with hidden environmental degrees of freedom contributed to the output.
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The tracked particle is the observable part of the system. The fluid’s internal relaxation modes, molecular arrangements and other variables that are not directly measured form hidden degrees of freedom (hDoFs). They retain correlations with the particle after earlier particle–bath interactions. Those correlations are the environment’s memory.
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How the delayed-measurement protocol works
A single observation of the particle cannot generally reveal useful information about the bath memory. The experiment therefore uses two consecutive binary measurements separated by a controlled delay:
- Prepare the system. The particle is held in an optical potential while it undergoes thermal motion in the viscoelastic fluid.
- Make the first binary measurement. The particle’s observable state is classified into one of two outcomes. This measurement records the particle but does not by itself expose the hidden bath state.
- Wait for a chosen delay. During the delay, the hidden bath variables continue to evolve. Information that was distributed into the environment can partially return to the particle.
- Make the second binary measurement. The later outcome can contain information that has flowed back from the hidden degrees of freedom. The pair of outcomes therefore carries memory information that a single measurement misses.
- Apply feedback. The protocol uses the measured outcome to control the particle’s potential and perform an extraction step. The feedback converts the accessible correlation into work.
- Repeat and estimate averages. The reported averages for Figure 4 were obtained from approximately 100 extraction protocols; shaded regions represent standard errors of the mean calculated by bootstrapping.
The delay is essential. It gives the bath time to return information to the observable particle, making the second measurement a minimal way to probe non-Markovian memory.
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Markovian and non-Markovian operation compared
| Question | Markovian environment | Non-Markovian environment in this experiment |
|---|---|---|
| What happens to environmental information? | It is effectively lost from the system’s future dynamics; the environment is treated as memoryless. | Hidden bath variables retain correlations with previous system–environment interactions. |
| Can information return to the observable particle? | No useful information backflow is expected in the ideal memoryless description. | Yes. After a delay, information can flow back from hidden bath degrees of freedom. |
| Is one measurement sufficient? | A single measurement is the natural information input for the memoryless protocol. | No. The demonstrated protocol needs two binary measurements separated by a controlled delay. |
| Can extracted work exceed the Markovian benchmark? | The work is limited by the optimum for the corresponding memoryless system. | In one measured state, the maximum extracted work exceeded that benchmark. |
Why information backflow can increase extracted work
Feedback engines use information about a fluctuating system to choose a favorable operation. In a memoryless bath, the useful information available after a measurement is constrained by the particle’s current state. With a memory-bearing bath, the particle and hidden environment can remain correlated. A later measurement can reveal part of that correlation after it has flowed back into the observable coordinate.
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The extra work is not energy created from nothing. The protocol is isothermal and feedback-controlled: it accesses free energy already associated with correlations and nonequilibrium structure in the particle–bath state. The authors discuss a total excess-free-energy bound that limits how much work any protocol can obtain from the available memory.
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This is why the result is best described as work extraction from environmental memory, not unrestricted “energy from information.” The experiment operates on a microscopic Brownian particle under controlled laboratory conditions and does not demonstrate a household power source or macroscopic continuous generation.
What the result establishes—and what it does not
- Established: a viscoelastic bath can supply experimentally usable information backflow; a two-measurement feedback protocol can extract work from it; and the measured maximum can exceed the corresponding Markovian optimum.
- Not established: a general-purpose energy technology, a net power plant, or an unlimited source of energy. The work comes from the system’s thermal and nonequilibrium resources and is bounded by the available excess free energy.
- Scope: the result concerns an optically trapped microscopic particle, a time-correlated fluid and carefully controlled measurements and potential changes.
Why the protocol is deliberately minimal
The two-measurement scheme demonstrates access to memory without requiring a complete reconstruction of every bath variable. The authors note that additional measurements, finer spatial resolution or more flexible control of the trapping potential could expose more of the available information and increase extracted work, while remaining subject to the total excess-free-energy bound.
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That design also clarifies the trade-off. More observations may reveal more correlations, but they add measurement complexity and can alter the dynamics. The reported experiment establishes the principle with the smallest delayed-measurement protocol needed to make information backflow operational.
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The associated Zenodo record includes raw trajectory data, MATLAB classes and scripts, figure-generation code, statistical-analysis files, reconstructed potentials and source data for the manuscript’s main and supplementary figures. These materials allow the trajectory processing, potential reconstruction and reported uncertainty analysis to be examined independently.
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Background for the information theory
The information-theory language used to describe the engine includes mutual information: a measure of how strongly two variables predict one another. Cover and Thomas’s Elements of Information Theory (Wiley, 1999), cited by the authors, provides the mathematical foundations for mutual information and related concepts. In this experiment, the relevant point is physical: correlations between the particle and hidden bath variables can become useful only when the measurement-and-feedback sequence captures them.
What comes next
The result gives researchers a way to treat non-Markovianity as a resource rather than only as a source of modeling error. Future information engines could test different delay times, measurement resolutions and control protocols to determine how much of an environment’s hidden free energy is accessible. Any gain must still be evaluated against the energetic cost of preparing, measuring and controlling the microscopic system.
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