Fourier Analysis of Parametrized Interactive Quantum Classifiers
Read the original on arXiv Machine Learning →The paper derives a closed‑form expression for the reduced quantum channel of a single‑qubit Interactive Quantum Classifier (IQC), revealing that Hamiltonian parameters directly control the constant, sine, and cosine components of the classifier output. This Fourier interpretation motivates a generalized family of Hamiltonian encodings, including matrix‑parameterized environmental Hamiltonians that produce non‑separable Fourier structures. Numerical experiments on synthetic and real‑world datasets demonstrate that these generalized encodings improve classification performance on several nonlinear benchmarks, with a simpler four‑parameter extension achieving comparable results with fewer trainable parameters.
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