Physical Computing at the Data Processing Inequality Limit

TL;DR

This paper introduces a theoretical framework for physical computing that maximizes information extraction from waveforms at the data processing inequality limit, significantly improving sensing accuracy.

Contribution

It establishes a new theoretical framework for physical computing that avoids information loss and maximizes information capture during sensing processes.

Findings

Achieves 100% sensing accuracy in electromagnetic experiments.

Outperforms traditional sensing paradigms by utilizing multiple waveform dimensions.

Provides a theoretical foundation for next-generation intelligent sensing systems.

Abstract

Wave-physics-based intelligent sensing has driven multidisciplinary applications from smart industries to decision-making systems. Traditional sensing paradigms transform physical waveforms into human-understandable intermediate representations through preprocessing. Such transformations inherently cause information loss owing to data processing inequality (DPI). Here, we established a theoretical framework for physical computing at the DPI upper limit. Physical computing avoids information loss during preprocessing by directly extracting information from physical waveforms, achieving the theoretical maximum of accessible information as determined by the DPI. Furthermore, physical computing comprehensively utilizes multiple dimensions of physical waveforms, thereby enhancing the upper limit of information capture capability. Electromagnetic sensing experiments have demonstrated that physical computing can achieve 100% sensing accuracy, substantially outperforming traditional sensing paradigms. The proposed theoretical framework of physical computing offers a promising path towards enhancing the information-capture capability of next-generation intelligent sensing systems.