arXiv:2611.09593v1 Announce Type: new Abstract: Quantum linear system algorithms offer theoretical potential for solving large sparse systems from PDE discretizations. However, applying them directly to fine-scale problems remains impractical on near-term hardware because of limited qubits, restricted circuit depth, and noise. They also prepare a quantum state rather than solution coefficients, whose recovery requires measurement and reconstruction. In this work, we develop a hybrid quantum-classical coarse-to-fine finite element framework for heterogeneous Darcy flow. We investigate whether a measured quantum coarse solution improves fine-grid initialization and reduces subsequent classical solver work. Rather than solve the full fine-scale system on a quantum device, we apply a method inspired by the Variational Quantum Linear Solver (VQLS) to the Galerkin coarse system. We reconstruct the approximate coarse solution from measurements, prolongate it to the fine grid, and use it to initialize a classical preconditioned iterative solve.} Numerical experiments on steady heterogeneous Darcy flow and a time-dependent SPE10-based problem show that measured quantum coarse solutions can improve fine-grid initialization, while classical correction recovers accurate pressure fields. The effect on classical solver iterations depends on the problem. All quantum computations were simulated using Qiskit Aer.