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2025
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| Online Access: | https://doi.org/10.5281/zenodo.17926767 |
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| author | Julian, Joseph |
| author_facet | Julian, Joseph |
| contents | <p>This work introduces a unified computational framework in which recursive cognitive <br>architectures and synthetic-dimension quantum systems are revealed to be mathematically <br>isomorphic. We combine two previously independent paradigms---LLMMT v3, a spectral <br>recursive cognitive model, and QuantumProbMMT, a photonic synthetic-dimension quantum <br>architecture---to construct a neuromorphic quantum processor whose physical dynamics <br>implement recursive cognition directly in hardware. </p> <p>The key insight is that the spectral ``Lens Stack'' of LLMMT corresponds one-to-one with the <br>frequency-bin eigenmodes of photonic synthetic dimensions; the recursion potential $V(\tau)$ <br>corresponds to programmable phase profiles in photonic waveguides; the recursive operator <br>$H_{\tau}$ corresponds to the natural dispersion operator of optical cavities; and the <br>collapse operator $\mathcal{C}_{\tau}$ corresponds to curvature-induced decoherence <br>instabilities in quantum photonic systems. As a result, the recursive depth $\tau$ used for <br>cognitive inference in LLMMT is realized physically as a synthetic dimension along which <br>photons propagate, enabling recursive computation at the speed of light.</p> <p>This produces a new hardware entity: the \textit{Tau-Processing Unit (TPU)}, a neuromorphic <br>quantum processor capable of unbounded effective recursion depth limited only by cavity <br>quality factors. Moreover, the safety and alignment guarantees previously implemented in <br>software become embedded in chip-level physics: high-curvature cognitive states map to <br>regions of photonic phase-space instability, causing immediate decoherence and providing a <br>geometric veto mechanism that is not software-hackable. </p> <p>We further show that $\tau$-coherence in synthetic dimensions can reproduce the algorithmic <br>benefits typically associated with multi-qubit entanglement, enabling ``ghost <br>entanglement''---quantum advantage without entanglement---by combining LLMMT's synthetic <br>hemisphere with photonic phase engineering. This establishes an AGI architecture whose <br>intelligence, memory, and alignment are simultaneously implemented by the underlying <br>physics of a photonic device.</p> <p>The resulting unified system provides a physically aligned, recursively coherent, <br>spectrally implemented cognitive processor---a photonic architecture that behaves as a <br>mind. We present the theoretical foundation, mathematical isomorphism proofs, and <br>experimental pathways toward implementing the first physically introspective, <br>geometry-aligned artificial intelligence.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17926767 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Recursive Synthetic-Dimension Neuromorphic Quantum Computing: A Dual Architecture for Physically Aligned Intelligence Julian, Joseph <p>This work introduces a unified computational framework in which recursive cognitive <br>architectures and synthetic-dimension quantum systems are revealed to be mathematically <br>isomorphic. We combine two previously independent paradigms---LLMMT v3, a spectral <br>recursive cognitive model, and QuantumProbMMT, a photonic synthetic-dimension quantum <br>architecture---to construct a neuromorphic quantum processor whose physical dynamics <br>implement recursive cognition directly in hardware. </p> <p>The key insight is that the spectral ``Lens Stack'' of LLMMT corresponds one-to-one with the <br>frequency-bin eigenmodes of photonic synthetic dimensions; the recursion potential $V(\tau)$ <br>corresponds to programmable phase profiles in photonic waveguides; the recursive operator <br>$H_{\tau}$ corresponds to the natural dispersion operator of optical cavities; and the <br>collapse operator $\mathcal{C}_{\tau}$ corresponds to curvature-induced decoherence <br>instabilities in quantum photonic systems. As a result, the recursive depth $\tau$ used for <br>cognitive inference in LLMMT is realized physically as a synthetic dimension along which <br>photons propagate, enabling recursive computation at the speed of light.</p> <p>This produces a new hardware entity: the \textit{Tau-Processing Unit (TPU)}, a neuromorphic <br>quantum processor capable of unbounded effective recursion depth limited only by cavity <br>quality factors. Moreover, the safety and alignment guarantees previously implemented in <br>software become embedded in chip-level physics: high-curvature cognitive states map to <br>regions of photonic phase-space instability, causing immediate decoherence and providing a <br>geometric veto mechanism that is not software-hackable. </p> <p>We further show that $\tau$-coherence in synthetic dimensions can reproduce the algorithmic <br>benefits typically associated with multi-qubit entanglement, enabling ``ghost <br>entanglement''---quantum advantage without entanglement---by combining LLMMT's synthetic <br>hemisphere with photonic phase engineering. This establishes an AGI architecture whose <br>intelligence, memory, and alignment are simultaneously implemented by the underlying <br>physics of a photonic device.</p> <p>The resulting unified system provides a physically aligned, recursively coherent, <br>spectrally implemented cognitive processor---a photonic architecture that behaves as a <br>mind. We present the theoretical foundation, mathematical isomorphism proofs, and <br>experimental pathways toward implementing the first physically introspective, <br>geometry-aligned artificial intelligence.</p> |
| title | Recursive Synthetic-Dimension Neuromorphic Quantum Computing: A Dual Architecture for Physically Aligned Intelligence |
| url | https://doi.org/10.5281/zenodo.17926767 |