Data for: Quantum algorithm for one quasi-particle excitations in the thermodynamic limit via cluster-additive block-diagonalization

This record contains the numerical data underlying all figures in the manuscript "Quantum algorithm for one-quasiparticle excitations in the thermodynamic limit via cluster-additive block diagonalization" by Sumeet, M. Hörmann, and K. P. Schmidt (Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg), published in Physical Review Research. The work introduces a hybrid quantum-classical method for computing one-quasiparticle (1QP) excitation energies in the thermodynamic limit, using the variational quantum eigensolver (VQE) as a cluster solver within numerical linked-cluster expansions (NLCE) and applying the projective cluster-additive transformation (PCAT) to enforce cluster additivity. The data covers three models in the high-field phase: the one-dimensional transverse-field Ising model (TFIM) at J = 1.0, h = 1.0 (Figs. 6, 7); the two-dimensional TFIM on the square lattice at J = 0.328, h = 1.0 (Figs. 8, 9); and the one-dimensional TFIM with longitudinal field (TFIM+LF) at J = 0.5, h = 1.0, h_l = 0.5 (Figs. 10 to 14). VQE results use the Hamiltonian variational ansatz (HVA) at two circuit depths (N and N/2 layers) and the cost functions defined in Eqs. 30 to 32 of the paper. Contents: data/TFIM+LF/: raw VQE optimization data for the TFIM+LF model, as JLD2 (Julia, HDF5-based) binary files data/TFIM+LF_Heff/: derived effective Hamiltonian matrices (CSV) that feed the NLCE dispersion calculations, plus optimizer-iteration data used for the convergence analysis data/TFIM/1D/ and data/TFIM/2D/: VQE and exact-diagonalization (ED) results for the 1D chain and 2D square-lattice clusters (CSV) A README is included describing the directory layout, file-naming conventions, JLD2 keys, the cost-function and layer labels, and the procedure for reproducing each dispersion figure from the effective-Hamiltonian files. The data was generated in Julia (JLD2.jl, Optimization.jl with the Optim.jl ConjugateGradient backend, ForwardDiff.jl, Arpack.jl, and related packages). JLD2 files require JLD2.jl; CSV files can be read with any standard tool (Julia DelimitedFiles, Python numpy or pandas).