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<p>Behavioral biometric authentication from keystroke and smartphone-based gait signals is inherently a genuine-only learning problem under session variability and bounded neuromotor adaptation. We formulate this setting as one-class reconstruction in a fixed descriptor space and propose a Quantum–Classical Adaptive Autoencoding (QCAA) framework for modeling user-consistent behavioral structure. The framework integrates deterministic time-series feature construction, QUBO-based structured sparsification for circuit-width regulation, shallow variational quantum embedding, and measurement-induced reconstruction within a unified hybrid architecture. Reconstruction is derived directly from observable statistics, enabling anomaly scoring in the original feature domain while incorporating nonlinear Hilbert-space interactions. A controlled five-session dataset comprising fixed-text typing, free-text typing, and smartphone-based gait signals from 100 participants was constructed to evaluate longitudinal behavioral stability. Under session-partitioned one-class evaluation in the primary noise-aware simulation setting, the optimized QCAA achieves equal error rates as low as 2.60% for fixed-text typing while maintaining consistently competitive performance across free-text and gait modalities. The QUBO-based sparsification stage substantially reduces effective circuit width, improves descriptor compactness, and preserves reconstruction stability under realistic noise conditions. Complementary experiments on real IBM Quantum hardware were conducted solely as feasibility validations rather than large-scale performance benchmarks. These experiments demonstrate that the proposed hybrid pipeline remains operational under contemporary Noisy Intermediate-Scale Quantum (NISQ) conditions, although performance is affected by hardware noise, limited qubit availability, decoherence effects, and execution constraints. Collectively, the results provide an empirical assessment of hybrid quantum embeddings for reconstruction-based behavioral anomaly detection, illustrating both their potential benefits and their current practical limitations within near-term quantum computing environments.</p>

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Quantum-classical adaptive autoencoding of time-series signals for secure biometric authentication

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Sandip Dutta,
  • Biswajit Basu,
  • Soumen Roy,
  • Utpal Roy

摘要

Behavioral biometric authentication from keystroke and smartphone-based gait signals is inherently a genuine-only learning problem under session variability and bounded neuromotor adaptation. We formulate this setting as one-class reconstruction in a fixed descriptor space and propose a Quantum–Classical Adaptive Autoencoding (QCAA) framework for modeling user-consistent behavioral structure. The framework integrates deterministic time-series feature construction, QUBO-based structured sparsification for circuit-width regulation, shallow variational quantum embedding, and measurement-induced reconstruction within a unified hybrid architecture. Reconstruction is derived directly from observable statistics, enabling anomaly scoring in the original feature domain while incorporating nonlinear Hilbert-space interactions. A controlled five-session dataset comprising fixed-text typing, free-text typing, and smartphone-based gait signals from 100 participants was constructed to evaluate longitudinal behavioral stability. Under session-partitioned one-class evaluation in the primary noise-aware simulation setting, the optimized QCAA achieves equal error rates as low as 2.60% for fixed-text typing while maintaining consistently competitive performance across free-text and gait modalities. The QUBO-based sparsification stage substantially reduces effective circuit width, improves descriptor compactness, and preserves reconstruction stability under realistic noise conditions. Complementary experiments on real IBM Quantum hardware were conducted solely as feasibility validations rather than large-scale performance benchmarks. These experiments demonstrate that the proposed hybrid pipeline remains operational under contemporary Noisy Intermediate-Scale Quantum (NISQ) conditions, although performance is affected by hardware noise, limited qubit availability, decoherence effects, and execution constraints. Collectively, the results provide an empirical assessment of hybrid quantum embeddings for reconstruction-based behavioral anomaly detection, illustrating both their potential benefits and their current practical limitations within near-term quantum computing environments.