Structured execution of variational quantum circuits across learning and physical applications

This preprint evaluates a guarded execution approach for variational quantum circuits, combining compiled local causal cones, fixed-particle-number sparse-Hamiltonian adjoints, and repeated-block powering. We test the implementation across 14 molecular energy active spaces, closed excitation-transfer dynamics, and wide local learning circuits (100–300 qubits) on datasets like WDBC, diabetes, and ESOL. The findings outline specific performance gains and speedups under strict cost boundaries, while clearly documenting process overhead, resource limits, and predictive comparisons against standard frameworks like NumPy and Qulacs.