NodaPathlight: An Open-Source Mathematical Control Protocol for Programmable Optical Computing

NodaPathlight: An Open-Source Mathematical Control Protocol for Programmable Optical Computing Recent advances in experimental photonics suggest that the physical foundations may now exist for a different approach to computing, based on the predictable modification of material states by light. NodaPathlight explores how these experimentally established phenomena might be organized into a programmable computing architecture. The central proposition is that optical signals can induce controlled changes in a material's physical state. These changes can persist, providing memory, while the resulting material configuration determines how subsequent optical signals are transformed. By organizing these interactions through a mathematical control protocol, it may be possible to implement sequential computation using the material itself as both a storage and processing medium. Building on published research into optical phase-change memory, material-state logic and photonic signal processing, this white paper proposes a computational framework for implementing such an architecture. The accompanying open-source Python code investigates material-state transitions, optical propagation, signal routing, energy requirements, computational sequences and the conditions necessary for feedback-controlled operations. The work does not claim to demonstrate a functioning optical processor or introduce new physical laws. Although its underlying mechanisms have experimental precedents, their integration into the proposed architecture remains subject to important physical and engineering constraints. The contribution is therefore a mathematical and computational proposal, distinguishing established experimental evidence from conditional constructions and identifying the measurements required to evaluate its feasibility. The purpose of this publication is to make the proposed architecture and its computational implementation openly available for examination, reproduction, criticism and further development. The progress of experimental photonics makes this direction worth investigating. Whether these mechanisms can ultimately support a practical programmable optical computer remains an open question. This work is offered as an alternative approach for scientific exploration, experimental investigation and experimental collaboration.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-08
DOI
https://doi.org/10.5281/zenodo.24047040
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

NodaPathlight: An Open-Source Mathematical Control Protocol for Programmable Optical Computing

Radu Necsanu
2 links
Zenodo (CERN European Organization for Nuclear Research)
Neural Networks and Reservoir Computing
article

NodaPathlight: An Open-Source Mathematical Control Protocol for Programmable Optical Computing

Radu Necsanu
article en

Abstract

NodaPathlight: An Open-Source Mathematical Control Protocol for Programmable Optical Computing Recent advances in experimental photonics suggest that the physical foundations may now exist for a different approach to computing, based on the predictable modification of material states by light. NodaPathlight explores how these experimentally established phenomena might be organized into a programmable computing architecture. The central proposition is that optical signals can induce controlled changes in a material's physical state. These changes can persist, providing memory, while the resulting material configuration determines how subsequent optical signals are transformed. By organizing these interactions through a mathematical control protocol, it may be possible to implement sequential computation using the material itself as both a storage and processing medium. Building on published research into optical phase-change memory, material-state logic and photonic signal processing, this white paper proposes a computational framework for implementing such an architecture. The accompanying open-source Python code investigates material-state transitions, optical propagation, signal routing, energy requirements, computational sequences and the conditions necessary for feedback-controlled operations. The work does not claim to demonstrate a functioning optical processor or introduce new physical laws. Although its underlying mechanisms have experimental precedents, their integration into the proposed architecture remains subject to important physical and engineering constraints. The contribution is therefore a mathematical and computational proposal, distinguishing established experimental evidence from conditional constructions and identifying the measurements required to evaluate its feasibility. The purpose of this publication is to make the proposed architecture and its computational implementation openly available for examination, reproduction, criticism and further development. The progress of experimental photonics makes this direction worth investigating. Whether these mechanisms can ultimately support a practical programmable optical computer remains an open question. This work is offered as an alternative approach for scientific exploration, experimental investigation and experimental collaboration.

Zenodo (CERN European Organization for Nuclear Research)
Neural Networks and Reservoir Computing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon