The convergence of quantum computing and machine learning

H. Meenal, Md. Shoeb Atthar, Harika Koormala +1 more

Dipesh Niraula, Jamalina Jamaluddin, Julia M. Pakela +1 more

James C. L. Chow

Medical decision-making is increasingly integrating quantum computing (QC) and machine learning (ML) to analyze complex datasets, improve diagnostics, and enable personalized treatments. While QC holds the potential to accelerate optimization, drug discovery, and genomic analysis as hardware capabilities advance, current implementations remain limited compared to classical computing in many practical applications. Meanwhile, ML has already demonstrated significant success in medical imaging, predictive modeling, and decision support. Their convergence, particularly through quantum machine learning (QML), presents opportunities for future advancements in processing high-dimensional healthcare data and improving clinical outcomes. This review examines the foundational concepts, key applications, and challenges of these technologies in healthcare, explores their potential synergy in solving clinical problems, and outlines future directions for quantum-enhanced ML in medical decision-making.

Jincheng Zhang

The convergence of machine learning models is a significant challenge, often requiring extensive computational resources and prolonged training times. Traditional methods often struggle to achieve optimal performance within a reasonable timeframe. This paper proposes Inverse-Quantum-Classical Convergence (IQCC) as a novel approach to address this bottleneck, leveraging the synergistic potential of quantum and classical computational resources during training. We develop a dynamic weighting mechanism that intelligently orchestrates the contribution of each computational paradigm, resulting in significantly faster convergence and improved model performance. This method offers a promising pathway towards more efficient and scalable machine learning.

Narjes Ansari, César Feniou, Nicolaï Gouraud +13 more

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.

Maria Schuld, Francesco Petruccione