A Resource-Aligned Hybrid Quantum-Classical Framework for Multimodal Face Anti-Spoofing
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866908920159666176 |
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| author | Sun, Wanqi Xu, Jungang Duan, Chenghua |
| author_facet | Sun, Wanqi Xu, Jungang Duan, Chenghua |
| contents | Embedding high-dimensional data into resource-limited quantum devices remains a significant challenge for practical quantum machine learning. In multimodal face anti-spoofing, while linear compression methods such as principal component analysis can reduce dimensionality to accommodate limited quantum budgets, such approaches often lose critical high-order cross-modal correlations due to the loss of structural information. To this end, we propose a hybrid Matrix Product State (MPS)-Variational Quantum Circuit (VQC) framework, where the MPS serves as a structured, differentiable pre-quantum compression and fusion module, and the VQC acts as the quantum classifier. Built upon the low-rank structure controlled by the virtual bond dimension and integrated with a configurable nonlinear enhancement mechanism, this MPS module explicitly models long-range cross-modal correlations while compressing multimodal data into a compact representation matching the quantum budget and improving numerical stability under extreme compression. Experiments on the CASIA-SURF benchmark demonstrate that MPS-VQC achieves accuracy comparable to strong classical neural network baselines with fewer than 0.25M parameters, highlighting the parameter efficiency of tensor-network representations for high-dimensional multimodal data under tight resource budgets. Leveraging the intrinsic compatibility between MPS structures and quantum circuit topology, this framework not only provides a viable technological pathway for efficient multimodal anti-spoofing on NISQ devices but also serves as a stepping stone toward fully quantum implementations of such tasks in the future. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_27852 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Resource-Aligned Hybrid Quantum-Classical Framework for Multimodal Face Anti-Spoofing Sun, Wanqi Xu, Jungang Duan, Chenghua Quantum Physics Embedding high-dimensional data into resource-limited quantum devices remains a significant challenge for practical quantum machine learning. In multimodal face anti-spoofing, while linear compression methods such as principal component analysis can reduce dimensionality to accommodate limited quantum budgets, such approaches often lose critical high-order cross-modal correlations due to the loss of structural information. To this end, we propose a hybrid Matrix Product State (MPS)-Variational Quantum Circuit (VQC) framework, where the MPS serves as a structured, differentiable pre-quantum compression and fusion module, and the VQC acts as the quantum classifier. Built upon the low-rank structure controlled by the virtual bond dimension and integrated with a configurable nonlinear enhancement mechanism, this MPS module explicitly models long-range cross-modal correlations while compressing multimodal data into a compact representation matching the quantum budget and improving numerical stability under extreme compression. Experiments on the CASIA-SURF benchmark demonstrate that MPS-VQC achieves accuracy comparable to strong classical neural network baselines with fewer than 0.25M parameters, highlighting the parameter efficiency of tensor-network representations for high-dimensional multimodal data under tight resource budgets. Leveraging the intrinsic compatibility between MPS structures and quantum circuit topology, this framework not only provides a viable technological pathway for efficient multimodal anti-spoofing on NISQ devices but also serves as a stepping stone toward fully quantum implementations of such tasks in the future. |
| title | A Resource-Aligned Hybrid Quantum-Classical Framework for Multimodal Face Anti-Spoofing |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2603.27852 |