The principles that govern physics govern computation and intelligence. The framework opens new approaches to quantum computing and to machine intelligence grounded in the same mathematics.
A quantum bit design that uses the balance dynamics of the framework to extend coherence times and reduce error correction overhead.
A new gate architecture in which logical operations are performed through the dynamics of the framework rather than through external control pulses.
Machine learning systems that seek balance rather than optimising for a single metric. This offers a structural path to alignment rather than a post-hoc fix.
Computation carried out through light, using the geometry of light propagation rather than the switching of transistors.
Quantum computing has been promised for thirty years. Real machines remain small, error-prone, and far from solving the problems they were meant to solve. Meanwhile, AI systems scale but become less interpretable and less aligned.
The framework approaches both from the same angle. If computation is a physical process, and if the framework describes physical processes accurately, then its principles should apply. They do. The applications follow.
We invite researchers, universities and institutions to engage with, test and extend this unified framework. The deepest problems in physics will yield only to collaboration.