Geodesics of Quantum Feature Maps on the Space of Quantum Operators

Abstract

Recent advancements in the discipline of quantum algorithms have displayed the importance of the geometry of quantum operators. Given this thrust, this paper develops a rigorous geometric framework to analyze how the Riemannian structure of data, under the manifold hypothesis, influences the subspace of quantum gates induced by quantum feature maps. While numerous encoding schemes have been proposed in quantum machine learning, little attention has been given to how data geometry is deformed when mapped into the Lie group of special unitary operators. Addressing this gap, we assume a point cloud forms a smooth Riemannian manifold and formally construct the induced Riemannian geometry of a broad class of Hamiltonian quantum feature maps, which encompasses the majority of derived schemes. Starting from first principles, we derive analytic and, consequently, computational formulae for fundamental geometric measurements, including curvature, volume forms, and harmonic maps, providing tools for systematic deformation analysis. Notably, the derivations of the formulae elucidates how changes along paths in the data manifold interplay to changes in the associated subspace of special unitary operators, thereby indicating a direct geometric effect of data on quantum circuits. This framework establishes the mathematical validity required for principled analysis beyond heuristic ansatz and enables future research into geometry-aware quantum algorithm design.