The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912999290175488 |
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| author | Ansari, Narjes Feniou, César Gouraud, Nicolaï Loco, Daniele Badreddine, Siwar Claudon, Baptiste Aviat, Félix Blazhynska, Marharyta Gasperich, Kevin Michel, Guillaume Traore, Diata Villot, Corentin Plé, Thomas Adjoua, Olivier Lagardère, Louis Piquemal, Jean-Philip |
| author_facet | Ansari, Narjes Feniou, César Gouraud, Nicolaï Loco, Daniele Badreddine, Siwar Claudon, Baptiste Aviat, Félix Blazhynska, Marharyta Gasperich, Kevin Michel, Guillaume Traore, Diata Villot, Corentin Plé, Thomas Adjoua, Olivier Lagardère, Louis Piquemal, Jean-Philip |
| contents | 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 accelerator for quantum chemistry data. By leveraging Hilbert space mapping, these systems can achieve true chemical accuracy while bypassing the heuristics of classical approximations. We show how this tripartite convergence optimizes the drug discovery pipeline, spanning from initial system preparation to ML-driven, high-fidelity simulations. Finally, we position quantum-enhanced sampling as the beyond GPU frontier for modeling reactive cellular systems and pioneering next-generation materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_17790 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery Ansari, Narjes Feniou, César Gouraud, Nicolaï Loco, Daniele Badreddine, Siwar Claudon, Baptiste Aviat, Félix Blazhynska, Marharyta Gasperich, Kevin Michel, Guillaume Traore, Diata Villot, Corentin Plé, Thomas Adjoua, Olivier Lagardère, Louis Piquemal, Jean-Philip Quantum Physics Machine Learning Chemical Physics 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 accelerator for quantum chemistry data. By leveraging Hilbert space mapping, these systems can achieve true chemical accuracy while bypassing the heuristics of classical approximations. We show how this tripartite convergence optimizes the drug discovery pipeline, spanning from initial system preparation to ML-driven, high-fidelity simulations. Finally, we position quantum-enhanced sampling as the beyond GPU frontier for modeling reactive cellular systems and pioneering next-generation materials. |
| title | The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery |
| topic | Quantum Physics Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2603.17790 |