Quantum-compatible Riemannian conjugate gradient for ground-state preparation
The paper introduces Quantum Conjugate Gradient Descent (QCGD), a method that extends Riemannian optimization to quantum ground-state preparation. QCGD improves convergence per iteration by combining steepest descent with information from previous search directions, outperforming baseline methods like DB-QITE in terms of energy reduction and circuit resources for specific problems.