General Theory of Process

In the paper's words

Modern science is currently fragmented into siloed disciplines—quantum mechanics, artificial intelligence, network science, and signal processing—each describing complex systems using disparate vocabularies. This manuscript proposes a general theory of process, asserting that these fields are not merely analogous but are topologically isomorphic. By synthesizing fifteen foundational studies, we identify six rigorous structural isomorphisms that map the mechanics of reality across substrates.

The abstract, verbatim, and its opening sentence is the claim. Every line below is quoted from the text.

Numbers it reports

1. This dynamic approach yields a 3.1x thermodynamic efficiency advantage in active computational work over digital baselines (though this metric excludes the basal metabolic cost of maintaining the biological substrate).

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2. By implementing an erasure protocol on a 12-qubit system, we demonstrated that even after erasing 75% of the central chain (the bulk), the mutual information between the remaining boundary qubits remained significantly non-zero ($I(L:R) \approx 0.42$) (Quni-Gudzinas, 2026c).

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3. By using machine learning to decode this halo, we can recover the state with >90% accuracy.

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4. Instead of searching for a rare particle, we can fabricate the topological halo that defines it (Quni-Gudzinas, 2026j).

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5. For a Bell state, the n-Bloch projection discards 100% of the entanglement information ($\Delta C = 1.0$), reducing a maximally entangled quantum state to a separable product state.

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6. Our resource estimation reveals a valley of death in the intermediate regime ($100 \lt n \lt 706$), where the constant overheads of the superior polylogarithmic algorithm result in a spacetime volume penalty of $\approx 2.44x$ compared to the inferior linear approach (Quni-Gudzinas, 2026e).

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How it is done

1. This approach allows us to leverage the mature mathematical tools of physics to solve the nascent problems of intelligence.

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2. The “laws” we derive from this data are the laws of the samples, not necessarily the laws of the source.

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3. We compute by braiding the halos, not the cores.

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4. This failure of intuition leads to “geometric frustration” in algorithm design.

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What would falsify it

1. The power of this hypothesis lies in its ability to predict phenomena in one field based on known laws in another.

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2. If we identify a structural isomorphism between black hole thermodynamics and error correction in quantum computers, we can predict that quantum computers will exhibit a “wet hair” phenomenon before we even build them.

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3. If the answer is no, then the model is an artifact, no matter how well it fits the training data.

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Where the claim stops

1. The Artifact Zone: Validity is topological; simplified models fail when their connectivity does not match the territory.

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2. Materials: Weyl semimetals host effective monopoles only when the topological gap $\Delta \gt k_B T$.

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3. 7.4 Future Directions: The Spectroscopic QPU We propose the construction of the spectroscopic QPU—a quantum processor that does not hide from the environment, but actively senses it.

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Where to read

1. Unifying17,427 words · 76 min