Research
Publications and ongoing work from the QCSA Research team, spanning quantum chemistry, quantum machine learning, and their applications to the sciences.
Comparing the Performance of Leading VQE Algorithms for Computing Ground-State Energies of Amino Acids
An integrated, reproducible benchmark of over 10 VQE ansatzes and two truncation methods for computing amino acid ground-state energies on NISQ hardware. The study evaluates noise resilience, barren-plateau trainability, adaptive vs. fixed ansatzes, and accuracy vs. expressive capacity using Hamiltonians from the QMProt Dataset.
Protein-Ligand Binding Affinity Prediction: Quantum Reservoir Computing as a Nonlinear Feature Map
A research framework for protein-ligand binding affinity prediction on the PDBbind refined set, exploring quantum reservoir computing as a fixed nonlinear feature map fused with classical deep learning. Presented as an honest proof-of-concept, the work transparently documents where the quantum approach falls short of classical baselines and outlines concrete paths toward genuine quantum advantage.
