Quantum Kernels for Audio Deepfake Detection Using Spectrogram Patch Features
Quantum machine learning has emerged as a promising tool for pattern recognition, yet many audio-focused approaches still treat spectrograms as generic images and do not explicitly exploit their time-frequency structure. We propose Q-Patch, a quantum feature map tailored to audio that encodes local time-frequency patches from mel-spectrograms into quantum states using shallow, hardware-efficient circuits with adjacency-aware entanglement. Each selected patch is summarized by a compact four-dimensional acoustic descriptor and mapped to a four-qubit circuit with depth at most three, enabling practical quantum kernel construction under near-term constraints. We evaluate Q-Patch on an audio s...
| Authors | Lisan Al Amin, Rakib Hossain, Mahbubul Islam et al. |
|---|---|
| Primary category | cs.SD |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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