Physics-Informed Residual Warm-Starting and Hardware-Noise Resilience in Transformer-Accelerated Molecular VQE (Version 2)

Version v6 Update: Physics-Informed Residual Warm-Starting and Hardware Noise Resilience This release (v6) significantly expands the preprint from v5 (DOI: 10.5281/zenodo.22013110) with out-of-distribution asymptotic stress testing, a hybrid physics-guided residual ML architecture, and realistic NISQ hardware-noise benchmarks for Transformer-accelerated Variational Quantum Eigensolver (VQE) molecular ground-state simulations. Summary of Version v6 Additions:1. Physics-Informed Residual Warm-Starting: Rather than predicting raw variational quantum circuit parameters directly, our transformer architecture predicts the residual correction relative to classical Hartree-Fock / MP2 perturbation theory anchors (θ_init = θ_HF + Δθ_residual). This bounds error in out-of-distribution stretched geometries.2. OOD Bond Dissociation Benchmarks (LiH & H2): Demonstrates bounded energy variance and reliable optimization convergence across non-equilibrium dissociation regimes where naive black-box neural networks diverge.3. Hardware Noise Resilience Study: Rigorous benchmarks across depolarizing channels, amplitude damping (T1 relaxation), and readout errors (epsilon in [0, 0.05]). Residual warm-starting mitigates barren plateaus and cuts cumulative gate error exposure by 35% to 52%.4. Manuscript Enhancements: Added Section 7 with empirical learning dynamics and noise decay profiles, accompanied by publication-quality vector figures. Code & Reproducibility:- GitHub: https://github.com/aashiq-parinda/quantum-genai-warmstart- Release Tag: v2.0.0 (matches v6 preprint) Citation:Khan, Ashraf. "Rigorous Generalization Study: Physics-Informed Residual Warm-Starting and Hardware-Noise Resilience in Transformer-Accelerated Molecular VQE." Zenodo (2026), Version v6. DOI: 10.5281/zenodo.22013110.