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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Machine learning at the mesoscale: A computation-dissipation bottleneck.

Alessandro Ingrosso¹, Emanuele Panizon¹

¹Quantitative Life Sciences, Abdus Salam International Centre for Theoretical Physics, 34151 Trieste, Italy.

Physical Review. E

|February 17, 2024

Summary

This summary is machine-generated.

Physical systems face a performance-energy trade-off in information processing. This study reveals how non-equilibrium conditions in mesoscopic systems enhance computation performance by managing this bottleneck.

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Area of Science:

Thermodynamics
Information Theory
Mesoscopic Physics

Background:

Information processing in physical systems inherently involves energetic costs, creating a performance-energy trade-off.
Mesoscopic systems, operating at the interface of quantum and classical physics, are crucial for understanding fundamental limits.
The efficiency of input-output devices is constrained by the principles of thermodynamics and information theory.

Purpose of the Study:

To formulate and investigate a computation-dissipation bottleneck in mesoscopic input-output devices.
To explore the role of non-equilibrium conditions in enhancing computational performance.
To elucidate the compromise between information compression, computation, and dynamic irreversibility.

Main Methods:

Development of a theoretical framework to model the computation-dissipation bottleneck.
Utilizing both real-world data sets and synthetic tasks for analysis.
Analysis of nonreciprocal interactions to understand induced dynamic irreversibility.

Main Results:

Demonstrated that non-equilibrium conditions lead to enhanced performance in mesoscopic systems.
Quantified the trade-off between information compression and input-output computation.
Identified the link between nonreciprocal interactions, dynamic irreversibility, and system performance.

Conclusions:

Non-equilibrium thermodynamics offers a pathway to overcome performance limitations in information processing.
The study provides a novel framework for understanding the fundamental limits of computation in physical systems.
Findings have implications for the design of efficient nanoscale devices and understanding biological information processing.