arXiv:2610.41690v1 Announce Type: new Abstract: We consider here computational homogenization of a periodic two-phase conduction problem. Quantum linear-system algorithms for its finite element discretization are limited by the condition number of the stiffness matrix. This condition number grows quadratically with the number of elements per side. Classically, preconditioning by the homogeneous (single-phase) operator bounds the condition number by the conductivity contrast, for every mesh size. On a quantum computer, however, a preconditioner encoded as a separate factor multiplies the subnormalizations, and the quadratic dependency returns. In this paper, we absorb the closed-form inverse square root of the homogeneous operator into the finite element factors. The preconditioned operator then becomes the product of a norm-preserving gradient operator $\mat{V}$, a diagonal matrix of scaled conductivities, and $\mat{V}^T$. Each factor has norm at most one. The block encoding therefore has subnormalization one, and its condition number is bounded by just the conductivity contrast, for every mesh size and in every dimension. The operator $\mat{V}$ is applied as a quantum Fourier transform, a small rotation at each wavenumber, and an inverse transform. Quantum singular value transformation then block-encodes the inverse of the preconditioned operator. We test the method numerically on three periodic cells with up to $10^6$ unknowns. The results confirm that the condition number does not depend on the mesh, and the energy-norm error of the inverse is below the polynomial tolerance at every mesh. On meshes of $4 \times 4$ and $8 \times 8$ elements, we also build the entire inverse as one Qiskit circuit and simulate it by state vector, with the rotation at each wavenumber built from uniformly controlled rotations. The output state agrees with the finite element solution to within the polynomial tolerance.