Using Classical-to-Quantum Feature Handoff to Characterize Decoherence Sensitivity in Parameterized Quantum Circuits

Quantum machine learning algorithms hold promise for near-term quantum advantage, yet their
performance under realistic hardware noise remains poorly characterized. In this work, we introduce a
hybrid classical-to-quantum transfer learning architecture in which a fixed linear encoder maps twodimensional classical input data to four quantum rotation angles, which are subsequently encoded into a
parameterized quantum circuit (PQC) ansatz executed on the PKTRON v3.7.3 simulation framework
using the PK NoisyLab 8Q virtual device. We systematically evaluate the degradation of binary
classification accuracy and the quantum feature space under three distinct decoherence channels —
amplitude damping, phase damping, and depolarizing noise — across five noise strength levels (p = 0.01
to 0.35) and three entanglement depths (L = 1, 2, 3 layers). Our results reveal that phase damping and
depolarizing noise impose an immediate, strength-invariant accuracy reduction of 10% from the ideal
baseline of 83.33%, while amplitude damping exhibits partial robustness at low noise levels. Frobenius
distance analysis demonstrates that depolarizing noise induces the steepest degradation of the quantum
feature space, reaching a saturation plateau at p = 0.20. Critically, deeper entanglement circuits achieve
higher ideal accuracy but exhibit superlinear growth in Frobenius distance under noise, suggesting a
fundamental accuracy-robustness tradeoff. We propose a Noise Sensitivity Score S(N) as a lightweight,
application-level noise diagnostic metric, offering a tractable alternative to full quantum process
tomography for NISQ-era devices.