A Hybrid Quantum-Classical Framework for Adaptive AI via Nonlinear Self-Reference
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Hello everyone, my name is Alperen. Today I’d like to introduce my first research paper: a hybrid quantum-classical evolutionary AI framework. The problem: Today’s AI is static. Current linear learning models (LLMs) evolve only through heavy and expensive retraining cycles. They suffer from the catastrophic forgetting problem and exhibit a “one-size-fits-all” behavior.
My Proposed Model: My proposed model is an AI agent that evolves continuously and in real-time alongside users. Instead of traditional gradient descent, the system uses a nonlinear self-reference term ($\mathcal{S}_t[\rho]$), which is incorporated into the dynamics of an open quantum system to enable an active memory function. How it works: > * A classical LLM generates multiple candidate responses. A quantum layer evaluates and scores their structural qualities using specific metrics ($\chi^2$ and $\zeta$). An offline evolutionary loop combines the best features using a special “Synergy Integral” ($S_{int}$), making the agent smarter over time without requiring a full retraining. I have just published a preprint detailing this theoretical architecture. I look forward to your feedback, hardware testing ideas, or mathematical critiques. For more details, the architecture, and equations, you can read my full paper below:📄 Preprint: [ https://doi.org/10.20944/preprints202603.1098.v1\] submitted by /u/Popular_Dig_9505 [link] [comments]