Canonical quantization of neurons

What the math gives to ML

The paper introduces a concrete nonlinear operator construction for quantum data: form a parameterized Hermitian Hamiltonian from fixed interaction operators, then apply an ordinary activation function to its spectrum through matrix functional calculus. The transferable asset is the interaction between noncommuting operators and spectral nonlinearities, which produces features that cannot be reduced to pointwise scalar activations or simultaneously diagonalized classical features. A practical first implementation is a small differentiable matrix model or quantum-data layer whose output is the expectation value of the activated Hamiltonian. The key falsifiable comparison is against equally parameterized commuting Hamiltonians and classical linear-energy neurons.