Information Processing Capacity of Stationary Physical Systems: Theory, Data-efficient Estimation Methods, and Photonic Demonstration

2026-05-20 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Engineering & Applied Sciences · Depth: Expert, extended

Summary

This work extends the Information Processing Capacity (IPC) framework to stationary physical computing systems, offering a principled, task-independent, and data-efficient method to characterize their computational capabilities. The authors establish that individual capacities are bounded between zero and one, their sum over a complete basis is bounded by the number of readouts, and noise strictly reduces this bound. They introduce data-efficient estimation methods, including Richardson extrapolation and Sobol quasi-random sampling, to correct for finite-sample bias. The framework is experimentally validated using a photonic computing system based on picosecond laser pulses propagating through a nonlinear optical fiber. By varying laser power and fiber length, systematic shifts of the IPC distribution toward higher-order nonlinear capacities, induced by the Kerr effect, are observed. The total IPC is shown to correlate strongly with performance on benchmark machine learning tasks like PCA-reduced MNIST and Two Spirals, and provides a reliable estimate of the system's effective dimensionality.

Key takeaway

For AI Scientists and Machine Learning Engineers evaluating physical computing hardware, this research provides a robust, task-independent metric. You should consider implementing the extended IPC framework to characterize the computational traits of stationary physical systems, especially when assessing their potential for specific machine learning tasks. The data-efficient estimation methods, particularly Sobol sampling and Richardson extrapolation, will help you obtain reliable capacity measurements even with limited experimental data, enabling more informed hardware design and selection decisions.

Key insights

The IPC framework effectively quantifies stationary physical systems' computational capabilities, linking intrinsic dynamics to machine learning performance.

Principles

• Individual IPCs are bounded between 0 and 1.
• Noise strictly reduces total IPC.
• Total IPC correlates with effective system dimensionality.

Method

Data-efficient IPC estimation uses Richardson extrapolation and Sobol quasi-random sampling to correct finite-sample bias and remove false-positive capacities, ensuring accurate measurement of computational traits.

In practice

• Use IPC to benchmark physical computing hardware.
• Adjust laser power/fiber length to tune system nonlinearity.
• Apply Sobol sampling for efficient data collection.

Topics

• Information Processing Capacity
• Stationary Physical Systems
• Photonic Computing
• Data-efficient Estimation Methods
• Kerr Effect

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.