parameterised-quantum-circuits

A reproducible toolkit that predicts, at design time, the first-order noise susceptibility of gradients in equivariant quantum neural networks, resolved by parameter and noise slot, validated against matched finite-difference derivatives, with engineered zero-response controls, signed coherent cases, and a parameter-resolved validation map.

A reproducible toolkit for auditing symmetry-organised complexity in equivariant quantum neural network ansatz, reporting sector occupation, cross-sector coherence, sectoral fluctuation, and generator-sum compliance against U(1), SU(2), and permutation symmetry before training.