Quantum hardware can preserve the mathematical geometry needed for quantum machine learning, but geometric fidelity and predictive performance are separate concerns—a quantum circuit can be geometrically accurate without improving classification accuracy.
This paper tests whether quantum computers can reliably preserve the geometric structure of data when using quantum kernel methods for machine learning.