QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing
Abstract
A central goal of quantum machine learning is to identify applications in which small quantum circuits can improve task performance while remaining executable on real hardware. We investigate deepfake (synthetic) speech detection, a largely unexplored application in this area, where reliable confidence scores are as important as discrimination performance because deployment decisions are made at fixed thresholds and overconfident errors can be costly. We introduce QBFF, which routes a compressed speech representation through a small 8-qubit variational circuit (VQC) used as a drop-in bottleneck, with a matched classical bottleneck as the control. The quantum circuit yields better-calibrated confidence in most matched comparisons, including out-of-distribution evaluation, though the downstream deployment-cost benefit is model-dependent. We then run the same circuit on a real IBM quantum processor. To our knowledge, this is the first anti-spoofing pipeline to run on a QPU; hardware execution demonstrates feasibility, with lower subset EER and Cllr but higher mean actDCF than the matched classical reference. Together, these results show that hardware-executable quantum bottlenecks can improve score reliability, with gains that vary by evaluation setting.
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BibTeX
@misc{firc2026qbff,
title = {{QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing}},
author = {Anton Firc and Nouhaila Innan and Vojtěch Staněk and Kamil Malinka and Martin Perešini and Zbyněk Lička and Walid El Maouaki and Alberto Marchisio and Muhammad Shafique},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.16390},
eprint = {2610.16390},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.16390},
}
RIS
TY - UNPB
TI - QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing
AU - Anton Firc
AU - Nouhaila Innan
AU - Vojtěch Staněk
AU - Kamil Malinka
AU - Martin Perešini
AU - Zbyněk Lička
AU - Walid El Maouaki
AU - Alberto Marchisio
AU - Muhammad Shafique
PY - 2026
DA - 2026/10/12
PB - arXiv
DO - 10.48550/arxiv.2610.16390
UR - https://arxiv.org/abs/2610.16390
AB - A central goal of quantum machine learning is to identify applications in which small quantum circuits can improve task performance while remaining executable on real hardware. We investigate deepfake (synthetic) speech detection, a largely unexplored application in this area, where reliable confidence scores are as important as discrimination performance because deployment decisions are made at fixed thresholds and overconfident errors can be costly. We introduce QBFF, which routes a compressed speech representation through a small 8-qubit variational circuit (VQC) used as a drop-in bottleneck, with a matched classical bottleneck as the control. The quantum circuit yields better-calibrated confidence in most matched comparisons, including out-of-distribution evaluation, though the downstream deployment-cost benefit is model-dependent. We then run the same circuit on a real IBM quantum processor. To our knowledge, this is the first anti-spoofing pipeline to run on a QPU; hardware execution demonstrates feasibility, with lower subset EER and C_llr but higher mean actDCF than the matched classical reference. Together, these results show that hardware-executable quantum bottlenecks can improve score reliability, with gains that vary by evaluation setting.
ER -