A Resource-Aligned Hybrid Quantum-Classical Framework for Multimodal Face Anti-Spoofing

• URL: http://arxiv.org/abs/2603.27852v1
• Date: Sun, 29 Mar 2026 20:12:47 GMT
• Title: A Resource-Aligned Hybrid Quantum-Classical Framework for Multimodal Face Anti-Spoofing
• Abstract summary: In multimodal face anti-spoofing, linear compression methods can reduce dimensionality to accommodate limited quantum budgets.<n>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.<n>We show that MPS-VQC achieves accuracy comparable to strong classical neural network baselines with fewer than 0.25M parameters.
• Score: 11.018914804434301
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Quantum-Enhanced Processing with Tensor-Network Frontends for Privacy-Aware Federated Medical Diagnosis [0.13999481573773068]
  The framework is motivated by two practical constraints in privacy-aware federated learning.<n>The MPC benchmark shows that communication cost is governed primarily by the dimension of the protected latent representation.<n>Results support a co-NIST perspective in which representation compression, post-aggregation quantum refinement, and privacy-aware deployment should be optimized jointly.
  arXiv  Detail & Related papers  (2026-04-02T04:52:31Z)
• Physics-Informed Hybrid Quantum-Classical Dispatching for Large-Scale Renewable Power Systems:A Noise-Resilient Framework [9.378801906395179]
  High-penetration energy introduces significantity and non-Classicality into power system dispatching optimization.<n>Existing approaches typically treat the power grid as a "black box"<n>This paper proposes a Hybrid Quantum-Bridging Dispatching (PIHQ-CD) framework.
  arXiv  Detail & Related papers  (2026-01-26T13:35:54Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• Reinforcement Learning for Quantum Network Control with Application-Driven Objectives [53.03367590211247]
  Dynamic programming and reinforcement learning offer promising tools for optimizing control strategies.<n>We propose a novel RL framework that directly optimize non-linear, differentiable objective functions.<n>Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
  arXiv  Detail & Related papers  (2025-09-12T18:41:10Z)
• TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing [50.95799256262098]
  We introduceHyper-VQC, a novel tensor-train (TT)-guided hypernetwork framework for quantum machine learning.<n>Our framework delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.<n>These results positionHyper-VQC as a scalable and noise-resilient framework for advancing practical quantum machine learning on near-term devices.
  arXiv  Detail & Related papers  (2025-08-01T23:37:55Z)
• Hybrid Quantum Classical Surrogate for Real Time Inverse Finite Element Modeling in Digital Twins [4.978621186982144]
  Large-scale civil structures, such as bridges, pipelines, and offshore platforms, are vital to modern infrastructure, where unexpected failures can cause significant economic and safety repercussions.<n>FE modeling is widely used for real-time structural health monitoring (SHM), its high computational cost and the complexity of inverse FE analysis pose ongoing challenges.<n>Here, we propose a hybrid quantum classical multilayer perceptron framework to tackle these issues.
  arXiv  Detail & Related papers  (2025-07-30T04:09:49Z)
• A purely Quantum Generative Modeling through Unitary Scrambling and Collapse [6.647966634235082]
  Quantum Scrambling and Collapse Generative Model (QGen) is a purely quantum paradigm that eliminates classical dependencies.<n>We introduce a measurement-based training principle that decomposes learning into tractable subproblems, mitigating barren plateaus.<n> Empirically, QGen outperforms classical and hybrid baselines under matched parameter budget, while maintaining robustness under finite-shot sampling.
  arXiv  Detail & Related papers  (2025-06-12T11:00:21Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Toward Large-Scale Distributed Quantum Long Short-Term Memory with Modular Quantum Computers [5.673361333697935]
  We introduce a Distributed Quantum Long Short-Term Memory (QLSTM) framework to address scalability challenges on Noisy Intermediate-Scale Quantum (NISQ) devices.<n>QLSTM captures long-range temporal dependencies, while a distributed architecture partitions the underlying Variational Quantum Circuits into smaller, manageable subcircuits.<n>We demonstrate that the distributed QLSTM achieves stable convergence and improved training dynamics compared to classical approaches.
  arXiv  Detail & Related papers  (2025-03-18T10:07:34Z)
• Practical hybrid PQC-QKD protocols with enhanced security and performance [44.8840598334124]
  We develop hybrid protocols by which QKD and PQC inter-operate within a joint quantum-classical network. In particular, we consider different hybrid designs that may offer enhanced speed and/or security over the individual performance of either approach.
  arXiv  Detail & Related papers  (2024-11-02T00:02:01Z)
• Quantum-Train with Tensor Network Mapping Model and Distributed Circuit Ansatz [0.8192907805418583]
  Quantum-Train (QT) is a hybrid quantum-classical machine learning framework. It maps quantum state measurements to classical neural network weights. Traditional QT framework employs a multi-layer perceptron (MLP) for this task, but it struggles with scalability and interpretability. We introduce a distributed circuit ansatz designed for large-scale quantum machine learning with multiple small quantum processing unit nodes.
  arXiv  Detail & Related papers  (2024-09-11T03:51:34Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.