The small engine exploits noise to turn information into fuel

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Engine info schematic. (A) Noisy detector measures position y From the bead already in the x. Ratchet based on either (b) noisy position measurement y or (c) a Bayesian position estimation (dashed blue circle). attributed to him: physical review messages (2022). DOI: 10.1103/ PhysRevLett.129.130601

Usually a lot of background noise is guaranteed to disrupt the work. But physicists have developed a tiny motor — made of a glass bead — that not only withstands the distracting effect of noise, but can harness it to run efficiently. Their experience was reported in the journal physical review messages It was selected by the journal as one of the most prominent research papers.

in Everyday life, we are familiar with engines and engines that take fuel to move in a directed manner and thus perform useful work. But things are more complicated in the microscopic world, where noise in the form of heat can easily spoil things.

“Heat makes components of small machines vibrate back and forth all the time,” senior author John Bechover explains, Quantum physicist at Simon Fraser University in Burnaby, British Columbia, and a member of the Foundational Questions Institute, FQXi, a physics research center. So usually the effect of such thermal noise from heat in the environment is to reduce the small amount of useful work engine can produce.

But there is a special family of tiny machines called “information actuators” that can actually exploit noise to move in a directed manner. The information engine works by measuring small motions generated by heat and using this information to selectively reinforce those motions that go in the “correct” direction, in the direction required by the machine.

“The information engine is a machine that turns information into action,” says Bechhoefer.

Physicists and engineers are excited to build such tiny actuators to harness information to design new microscopic machines for nanotechnology applications. “There is great interest in drawing inspiration from biomolecular machines developed by nature,” says co-author David Sivak, also a physicist at SFU. “Our work advances our understanding of how information is used in such machines, indicating possible uses for more efficient sustainable energy harvesting or computer storage and computation.”

“The information engine is a machine that turns information into action,” says John Bechhoefer.

Bechhoefer, Sivak and their colleagues at SFU Tushar Saha, Joseph Lucero and Jannik Ehrich built an information engine using a microscopic glass bead—the size of bacteria—suspended in water. The bead is held loosely in place by a laser beam that acts as a support under the beam. Molecules in the water gently scramble the granules, due to the natural thermal fluctuations in the liquid, oftentimes polishing the kernel upwards.

Here comes the trick: When the team measures that the bead has moved against gravity, due to thermal fluctuations, they raise the laser support. In this higher position, the beads now have more stored energy, or gravitational potential energy, like a ball being lifted, ready to fall.

The team didn’t have to put in the work to lift the particle; This movement occurred naturally thanks to the vibrations of the water molecules. So the motor converts the thermal heat from the water into stored gravitational mode energy using feedback about the movement of the bead to set the laser trap. “The decision as to whether to raise the trap, and if so, how much, depends on the information we gather about the position of the bead, which serves as ‘fuel’ for the engine,” says lead author Saha.

This is how it works in principle, but the correct implementation of the strategy is difficult if there is a lot of measurement noise, generated in the system from the brightness of the laser beam used to locate the bead. In such cases, the uncertainty in bead position per measurement can be greater than the bead motions produced by the vibrating water molecules. “Measurement noise leads to misleading feedback and thus deteriorates performance,” Saha says.

Bayesian information engine

Typical information engines use feedback algorithms that base decisions on the last measurement of bead position, but these decisions may be wrong when measurement errors are large. In their latest paper, the team wanted to investigate whether there was a way around this annoying problem.

They developed a feedback algorithm that relies not simply on a direct measurement of the last bead position – since this measurement can be inaccurate – but instead on a more accurate measurement of the last bead position that relied on all previous measurements. Thus this filtering algorithm was able to allow measurement errors in its estimation work – called “Bayesian estimation”.

“By combining several noisy measurements in a clever way that includes a model of bead dynamics, one can restore a more accurate estimate of true bead position, significantly mitigating performance losses,” Lucero says.

In their new experiment, it was reported in physical review messages, the team showed that an information engine that applies observations based on Bayesian estimates performs much better than typical information engines, when measurement errors are large. In fact, most typical information engines will stop, if the measurement errors are too large.

“We were surprised to find that when measurement errors cross a critical threshold, the naive engine can no longer function as a pure information engine: the best strategy is to simply put your hands up and do nothing,” says Erich. “By contrast, the Bayesian Information Engine is able to put out some small positive work no matter how much the measurement error is.”

There is a price to pay for the Bayesian Information Engine’s ability to extract power even with large measurement errors. Since the Bayesian engine uses information from all previous measurements, it needs more storage capacity and involves more information processing.

“The trade-off arises because reducing measurement errors increases the work that can be extracted from fluctuations, but it also increases information processing costs,” says Erich. Thus, the team found maximum efficiency at a medium level of measurement error, where they can achieve a good level of energy extraction, without requiring much processing.

“There is great interest in drawing inspiration from biomolecular machines developed by nature,” says David Sivak.

The team is now studying how things might change if the noise “fueling” the engine was caused by something other than heat. “We are preparing a research paper examining how optimal feedback strategy and performance changes when fluctuations are not just thermal,” but also arise due to the active consumption of energy in the surrounding environment, such as in living cells, Saha says. ”

more information:
Tushar K. Saha et al, Bayesian information engine that optimally exploits noisy measurements, physical review messages (2022). DOI: 10.1103/ PhysRevLett.129.130601

Provided by Foundational Questions Institute, FQXi

the quote: a small engine that exploits noise to turn information into fuel (2022, November 14) Retrieved on November 14, 2022 from

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