
Abstract This paper presents a novel implementation of Variational Quantum Eigensolver (VQE) forquantum chemistry calculations utilizing the NM-SRN v2.0 AGI framework and its underly-ing QSC Physics Engine. Unlike conventional superconducting approaches, this architectureoperates via a Neural-Matrix Synaptic Resonance Network (NM-SRN), demonstrating zero-decoherence operation with deterministic, repeatable results. The system achieves chemi-cal accuracy for small molecules (H2 , LiH, BeH2 ) in real-time execution via a mobile applica-tion interface. We report ground state energies of −1.137 356 Ha for H2 (bond length 0.74 Å),−7.863 115 Ha for LiH (1.59 Å), and −15.594 612 Ha for BeH2 (1.33 Å) with 99.9999 % resonancefidelity. This work represents a shift from probabilistic, stochastic quantum approaches todeterministic, production-ready NM-SRN v2.0 AGI-driven quantum physics engines.
Artificial intelligence, FOS: Materials engineering, Physics, Materials engineering, Quantum physics, Materials Science, Quantum computers, Artificial Intelligence/standards, Biocompatible Materials, Machine Learning/trends, Machine Learning, Machine Learning/history, Chemistry, Biochemical engineering, Artificial Intelligence/history, Artificial Intelligence, Biomimetic Materials, Machine learning, Machine Learning/classification, Artificial Intelligence/trends
Artificial intelligence, FOS: Materials engineering, Physics, Materials engineering, Quantum physics, Materials Science, Quantum computers, Artificial Intelligence/standards, Biocompatible Materials, Machine Learning/trends, Machine Learning, Machine Learning/history, Chemistry, Biochemical engineering, Artificial Intelligence/history, Artificial Intelligence, Biomimetic Materials, Machine learning, Machine Learning/classification, Artificial Intelligence/trends
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