hybrid-quantum-classical

QFoldAgent is a closed-loop multi-agent framework for quantum-classical protein structure prediction that iteratively optimizes Hamiltonian penalties using VQE and feedback, achieving improved RMSD and structural validity on 5-residue fragments.

This paper introduces IonSense-QKG, a metadata framework that enriches public lithium-ion battery datasets with quantum-readiness fields and a weighted Quantum Readiness Score to rank datasets for near-term hybrid quantum-classical machine learning.

This paper presents a hybrid quantum-classical pipeline using neutral-atom reservoir computing and auto-encoders for medical image classification, specifically for polyp detection. It addresses quantum measurement non-differentiability with a surrogate model to enable end-to-end training.