Computational quantum chemistry is a cornerstone of modern R&D, used across reaction design, catalysis, and materials discovery to deliver insights that complement painstaking and costly laboratory work. Because of this central role, the widespread claim that quantum chemistry will be the killer app for quantum computing carries significant responsibility. If quantum computers are to transform chemical sciences in meaningful ways, their performance must be evaluated against the full complexity and diversity of the problems that scientists confront in practice. In this study, we present a systematic evaluation of sample based quantum diagonalization (SQD) on the W4-11 thermochemistry benchmark, spanning 124 total atomization, 83 bond dissociation, 20 isomerization, 505 heavy atom transfer, and 13 nucleophilic substitution processes across diverse bonding situations and reaction mechanisms. We report the largest assessment to date of a quantum hybrid algorithm executed on a digital quantum device across a broad set of molecules and reactions, using 16.85 hours on the superconducting quantum processor ibm_rensselaer (a 127-qubit IBM Eagle processor) and 724.22 node hours on the AiMOS supercomputer, with commensurate resource allocation to enable fair classical versus quantum comparisons.