Selected work

Representative Papers

High-level quantum structured programs as quantum registers compositions

Aug 04, 2026

Current quantum programs predominantly rely on single-qubit gate operations and lack high-level abstractions, leading to complex and error-prone designs. This work proposes a structured programming paradigm that treats indivisible quantum registers as fundamental units, advancing computation through semantically precise register-level transformations and entanglement operations. To bridge high-level expressions with low-level semantics, the approach introduces an algebraic formal syntax. By integrating phase-conditioned operations, parallel evaluation mechanisms, and quantum SMT solving techniques, the framework enables a reliable mapping from high-level structured descriptions to low-level quantum semantics. This methodology substantially reduces programming complexity and establishes a foundation for scalable and robust quantum software systems.

0 citationsRead paper

Architecting Hybrid Quantum-Classical Software Systems: Exploration of the Design Trade-off Space with Quantitative Guarantees

Jun 23, 2026

This work addresses the challenge of meeting user quality-of-service (QoS) requirements under the constraints of noisy intermediate-scale quantum (NISQ) devices by proposing a service-oriented architecture for hybrid quantum-classical systems. It pioneers the integration of service-oriented architecture (SOA) with quantum computing, employing formal architectural style modeling and QoS-driven design space exploration to delineate architectural decision boundaries. The approach dynamically selects optimal execution strategies at both structural and behavioral levels in response to varying QoS demands. Experimental results demonstrate that the proposed method can dynamically configure the system under realistic NISQ constraints to deliver quantifiable performance guarantees aligned with user-specified QoS requirements.

0 citationsRead paper

Particle filtering methods for partially observed branching processes

May 20, 2026

This study addresses the challenge of parameter estimation in partially observed branching processes arising from incomplete surveillance data in epidemic modeling. The authors propose a Bayesian inference framework based on sequential Monte Carlo (SMC) methods, innovatively incorporating the Liu–West particle filter to dynamically update and estimate key model parameters. This approach effectively overcomes the limitations of conventional inference techniques under partial observability, significantly enhancing the computational tractability of partially observed branching processes in epidemiology while enabling rigorous quantification of parameter uncertainty. The method’s validity, effectiveness, and robustness are demonstrated through the replication and extension of canonical epidemic case studies, confirming its practical utility for real-world infectious disease modeling.

0 citationsRead paper

Information-Theoretic Digital Twins for Stealthy Attack Detection in Industrial Control Systems: A Closed-Form KL Divergence Approach

Mar 02, 2026

This work addresses the challenge of detecting stealthy false data injection attacks (FDIAs) in industrial control systems, which manipulate system states within physically plausible bounds and thus evade conventional detection. To overcome the limitations of deep learning methods—prone to overfitting—and traditional approaches—lacking scalability in high-dimensional settings—the authors propose a closed-loop information-theoretic digital twin framework. By integrating N4SID subspace identification with steady-state Kalman filtering, the framework introduces a closed-form KL divergence metric to quantify residual distribution shifts in real time, simultaneously capturing perturbations in both mean and covariance without requiring model training. Evaluated on the SWaT and WADI datasets, the method achieves F1 scores of 0.832 and 0.615, respectively, outperforming deep learning baselines such as TranAD while running approximately 600× faster on CPU with minimal memory footprint, making it suitable for GPU-less industrial edge controllers.

0 citationsRead paper

Graph Attention Networks with Physical Constraints for Anomaly Detection

Jan 18, 2026

This work addresses the unreliability and low interpretability of existing anomaly detection methods in water distribution networks, which often neglect network topology and physical laws. To overcome these limitations, the authors propose a novel approach that integrates residuals derived from mass and energy conservation principles as physics-informed features. For the first time, normalized violations of physical conservation laws are incorporated into a graph attention network, complemented by a bidirectional LSTM to capture spatiotemporal dependencies. A multi-scale module aggregates anomaly scores from individual nodes to the entire network level. Evaluated on the BATADAL dataset, the method achieves an F1 score of 0.979—3.3 percentage points higher than baseline approaches—and demonstrates strong robustness under 15% parameter noise, offering both high interpretability and generalization capability.

0 citationsRead paper

Recent publications

Latest Papers

High-level quantum structured programs as quantum registers compositions

Aug 04, 2026

Current quantum programs predominantly rely on single-qubit gate operations and lack high-level abstractions, leading to complex and error-prone designs. This work proposes a structured programming paradigm that treats indivisible quantum registers as fundamental units, advancing computation through semantically precise register-level transformations and entanglement operations. To bridge high-level expressions with low-level semantics, the approach introduces an algebraic formal syntax. By integrating phase-conditioned operations, parallel evaluation mechanisms, and quantum SMT solving techniques, the framework enables a reliable mapping from high-level structured descriptions to low-level quantum semantics. This methodology substantially reduces programming complexity and establishes a foundation for scalable and robust quantum software systems.

0 citationsRead paper

Architecting Hybrid Quantum-Classical Software Systems: Exploration of the Design Trade-off Space with Quantitative Guarantees

Jun 23, 2026

This work addresses the challenge of meeting user quality-of-service (QoS) requirements under the constraints of noisy intermediate-scale quantum (NISQ) devices by proposing a service-oriented architecture for hybrid quantum-classical systems. It pioneers the integration of service-oriented architecture (SOA) with quantum computing, employing formal architectural style modeling and QoS-driven design space exploration to delineate architectural decision boundaries. The approach dynamically selects optimal execution strategies at both structural and behavioral levels in response to varying QoS demands. Experimental results demonstrate that the proposed method can dynamically configure the system under realistic NISQ constraints to deliver quantifiable performance guarantees aligned with user-specified QoS requirements.

0 citationsRead paper

Particle filtering methods for partially observed branching processes

May 20, 2026

This study addresses the challenge of parameter estimation in partially observed branching processes arising from incomplete surveillance data in epidemic modeling. The authors propose a Bayesian inference framework based on sequential Monte Carlo (SMC) methods, innovatively incorporating the Liu–West particle filter to dynamically update and estimate key model parameters. This approach effectively overcomes the limitations of conventional inference techniques under partial observability, significantly enhancing the computational tractability of partially observed branching processes in epidemiology while enabling rigorous quantification of parameter uncertainty. The method’s validity, effectiveness, and robustness are demonstrated through the replication and extension of canonical epidemic case studies, confirming its practical utility for real-world infectious disease modeling.

0 citationsRead paper

Information-Theoretic Digital Twins for Stealthy Attack Detection in Industrial Control Systems: A Closed-Form KL Divergence Approach

Mar 02, 2026

This work addresses the challenge of detecting stealthy false data injection attacks (FDIAs) in industrial control systems, which manipulate system states within physically plausible bounds and thus evade conventional detection. To overcome the limitations of deep learning methods—prone to overfitting—and traditional approaches—lacking scalability in high-dimensional settings—the authors propose a closed-loop information-theoretic digital twin framework. By integrating N4SID subspace identification with steady-state Kalman filtering, the framework introduces a closed-form KL divergence metric to quantify residual distribution shifts in real time, simultaneously capturing perturbations in both mean and covariance without requiring model training. Evaluated on the SWaT and WADI datasets, the method achieves F1 scores of 0.832 and 0.615, respectively, outperforming deep learning baselines such as TranAD while running approximately 600× faster on CPU with minimal memory footprint, making it suitable for GPU-less industrial edge controllers.

0 citationsRead paper

Graph Attention Networks with Physical Constraints for Anomaly Detection

Jan 18, 2026

This work addresses the unreliability and low interpretability of existing anomaly detection methods in water distribution networks, which often neglect network topology and physical laws. To overcome these limitations, the authors propose a novel approach that integrates residuals derived from mass and energy conservation principles as physics-informed features. For the first time, normalized violations of physical conservation laws are incorporated into a graph attention network, complemented by a bidirectional LSTM to capture spatiotemporal dependencies. A multi-scale module aggregates anomaly scores from individual nodes to the entire network level. Evaluated on the BATADAL dataset, the method achieves an F1 score of 0.979—3.3 percentage points higher than baseline approaches—and demonstrates strong robustness under 15% parameter noise, offering both high interpretability and generalization capability.

0 citationsRead paper