Fourier Analysis of Parametrized Interactive Quantum Classifiers
Interactive Quantum Classifiers (IQCs) constitute a family of quantum machine learning models inspired by open quantum systems, in which the interaction between a target qubit and an environment is described by a Hamiltonian. Previous works introduced alternative Hamiltonian parameterizations and showed empirically that they can improve classification performance, but the role of these parameters in the resulting classifier remains poorly understood. In this work, we derive a closed-form expression for the reduced quantum channel generated by a parametrized IQC with a single target qubit. The analytical solution explicitly reveals how the Hamiltonian parameters control the constant, sine,...
| Authors | Fábio Novaes, Fernando M. de Paula Neto, João V. M. Cardoso |
|---|---|
| Primary category | quant-ph |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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