关键词: 关键词: 量子计算, 机器学习, 伪造语音, 注意力计算, 语音检测

Abstract: Voice forgery technology poses a potential threat to people's lives. Currently, classical fake speech detection models on the market face challenges such as performance improvement bottlenecks and excessive model parameters. To address these issues, a quantum‑Transformer based fake speech detection model—the Quantum Security Speech Model (QSSM)—was proposed. In this model, parameterized quantum circuits (PQC) were used to construct a quantum QKV mapping module for generating Query, Key, and Value vectors. The self‑attention computation between feature vectors was implemented via the Swap test, and quantum attention pooling based on PQC was employed to aggregate contextual information. Experimental results demonstrate that the quantum‑Transformer model reduces the equal error rate by 0.5% to 4.5% compared with classical models such as RawNet2 in fake speech detection tasks, while decreasing the parameter count by 43% relative to the classical Transformer model. This model provides a new pathway for deploying fake speech detection solutions in resource‑constrained environments.

Key words: quantum computing, machine learning, deepfake, attention module, Speech detection