[2603.17790] The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery

Quantum Physics arXiv:2603.17790 (quant-ph) [Submitted on 18 Mar 2026 (v1), last revised 2 Apr 2026 (this version, v3)] Title:The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery Authors:Narjes Ansari, César Feniou, Nicolaï Gouraud, Daniele Loco, Siwar Badreddine, Baptiste Claudon, Félix Aviat, Marharyta Blazhynska, Kevin Gasperich, Guillaume Michel, Diata Traore, Corentin Villot, Thomas Plé, Olivier Adjoua, Louis Lagardère, Jean-Philip Piquemal View a PDF of the paper titled The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery, by Narjes Ansari and 15 other authors View PDF Abstract:Integrating quantum mechanics into drug discovery marks a decisive shift from empirical trial-and-error toward quantitative precision. However, the prohibitive cost of ab initio molecular dynamics has historically forced a compromise between chemical accuracy and computational scalability. This paper identifies the convergence of High-Performance Computing (HPC), Machine Learning (ML), and Quantum Computing (QC) as the definitive solution to this bottleneck. While ML foundation models, such as FeNNix-Bio1, enable quantum-accurate simulations, they remain tethered to the inherent limits of classical data generation. We detail how High-Performance Quantum Computing (HPQC), utilizing hybrid QPU-GPU architectures, will serve as the ultimate acceler...