Selected work

Representative Papers

Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines

Aug 03, 2026

This study addresses the challenges of inaccurate remaining useful life (RUL) prediction for aircraft engines under dynamic combat missions, which leads to excessive maintenance costs and reduced operational readiness. To overcome this, we propose a deep learning–based predictive maintenance model that constructs multivariate sensor time-series inputs via a sliding window approach to automatically extract degradation features. The model is evaluated on the NASA C-MAPSS FD001 and FD004 datasets, demonstrating strong generalization under complex operating conditions: it achieves an R² of 0.8901, RMSE of 13.28, and a NASA score of 320.34 on FD001; an RMSE of 15.71 on FD004; and an AUC of 0.9973 for early warning within a critical 30-cycle threshold. These results significantly outperform baseline methods such as Random Forest and CNN-LSTM.

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Certifying Quantum Optimization and Circuit Cutting by Using Quantum-Classical Moment Duality

Jun 19, 2026

This work addresses the lack of general performance guarantees in existing quantum optimization algorithms and the difficulty of balancing efficiency with error control in circuit partitioning. It introduces, for the first time, a quantum–classical moment duality framework applicable to arbitrary quantum states. By leveraging a second-order sum-of-squares (SoS) semidefinite program, the approach establishes a duality between two-qubit Pauli-Z correlation matrices and the Goemans–Williamson relaxation, yielding certifiable lower bounds on Max-Cut values. Simultaneously, this correlation matrix reveals the tensor structure of quantum circuits, enabling efficient partitioning with rigorous error bounds. Experimental results demonstrate that near-optimal lower bounds can be achieved using only two-point correlation data, and the theoretical error bounds are empirically validated to hold tightly in practice.

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Mitigating Frequency Learning Bias in Quantum Models via Multi-Stage Residual Learning

Mar 10, 2026

This work addresses the spectral learning bias inherent in quantum machine learning models when approximating functions containing high- or multi-frequency components, which hinders their ability to capture complex spectral structures. To overcome this limitation, the study introduces, for the first time, a multi-stage residual learning framework into the quantum domain. By iteratively training additional parameterized quantum circuit modules to fit the residual error from the previous stage, the model progressively enhances its spectral representational capacity. Integrating quantum Fourier series approximation, diverse encoding strategies, and multi-qubit architectures, the proposed approach significantly reduces test mean squared error on synthetic multi-frequency benchmarks featuring Gaussian, Lorentzian, and triangular envelopes. This effectively mitigates the quantum Fourier parametrization bias and substantially improves learning performance on multi-frequency signals.

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Performance Evaluation of Dual RIS-Assisted Received Space Shift Keying Modulation

Nov 23, 2025

To address the limited signal routing flexibility in single-RIS indoor systems caused by static reflection, this paper proposes a cooperative dual-reconfigurable intelligent surface (RIS) architecture. The first RIS provides baseline channel enhancement, while the second RIS (RIS₂) dynamically adjusts its reflection phases according to source data bits—enabling bit-driven physical-layer spatial modulation and beam-level signal routing. This work pioneers the integration of spatial shift keying (SSK) with dynamic phase mapping across two RISs, establishing an end-to-end transmission framework under a multi-hop channel model. Experimental results demonstrate significant improvements in achievable capacity and substantial reduction in outage probability across varying carrier frequencies and inter-RIS distances, thereby enabling high-accuracy, data-dependent intelligent indoor wireless coverage.

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Quantum Voting Protocol for Centralized and Distributed Voting Based on Phase-Flip Counting

Oct 16, 2025

This paper addresses the challenges of anonymity, prevention of duplicate voting, and efficient tallying in quantum voting. We propose a dual-mode quantum voting protocol based on phase-flip counting, compatible with both centralized and distributed settings. Methodologically, we construct entangled candidate states using Hadamard and controlled-Z gates; votes are encoded via controlled-phase operations, and direct tallying is achieved through measurement of these candidate states—bypassing classical iterative aggregation. An entanglement-based verification mechanism is introduced to enhance security in remote voting. Our key contributions are: (i) the first integration of phase-flip encoding with multipartite entanglement to simultaneously guarantee voter anonymity and vote uniqueness; and (ii) support for quantum-parallel tallying, significantly improving scalability and efficiency in large-scale elections. The protocol is validated in scenarios with 4 voters/2 candidates and 8 voters/3 candidates, rigorously satisfying probability conservation, unbiased tallying, and cryptographic security requirements.

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Recent publications

Latest Papers

Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines

Aug 03, 2026

This study addresses the challenges of inaccurate remaining useful life (RUL) prediction for aircraft engines under dynamic combat missions, which leads to excessive maintenance costs and reduced operational readiness. To overcome this, we propose a deep learning–based predictive maintenance model that constructs multivariate sensor time-series inputs via a sliding window approach to automatically extract degradation features. The model is evaluated on the NASA C-MAPSS FD001 and FD004 datasets, demonstrating strong generalization under complex operating conditions: it achieves an R² of 0.8901, RMSE of 13.28, and a NASA score of 320.34 on FD001; an RMSE of 15.71 on FD004; and an AUC of 0.9973 for early warning within a critical 30-cycle threshold. These results significantly outperform baseline methods such as Random Forest and CNN-LSTM.

0 citationsRead paper

Certifying Quantum Optimization and Circuit Cutting by Using Quantum-Classical Moment Duality

Jun 19, 2026

This work addresses the lack of general performance guarantees in existing quantum optimization algorithms and the difficulty of balancing efficiency with error control in circuit partitioning. It introduces, for the first time, a quantum–classical moment duality framework applicable to arbitrary quantum states. By leveraging a second-order sum-of-squares (SoS) semidefinite program, the approach establishes a duality between two-qubit Pauli-Z correlation matrices and the Goemans–Williamson relaxation, yielding certifiable lower bounds on Max-Cut values. Simultaneously, this correlation matrix reveals the tensor structure of quantum circuits, enabling efficient partitioning with rigorous error bounds. Experimental results demonstrate that near-optimal lower bounds can be achieved using only two-point correlation data, and the theoretical error bounds are empirically validated to hold tightly in practice.

0 citationsRead paper

Mitigating Frequency Learning Bias in Quantum Models via Multi-Stage Residual Learning

Mar 10, 2026

This work addresses the spectral learning bias inherent in quantum machine learning models when approximating functions containing high- or multi-frequency components, which hinders their ability to capture complex spectral structures. To overcome this limitation, the study introduces, for the first time, a multi-stage residual learning framework into the quantum domain. By iteratively training additional parameterized quantum circuit modules to fit the residual error from the previous stage, the model progressively enhances its spectral representational capacity. Integrating quantum Fourier series approximation, diverse encoding strategies, and multi-qubit architectures, the proposed approach significantly reduces test mean squared error on synthetic multi-frequency benchmarks featuring Gaussian, Lorentzian, and triangular envelopes. This effectively mitigates the quantum Fourier parametrization bias and substantially improves learning performance on multi-frequency signals.

0 citationsRead paper

Performance Evaluation of Dual RIS-Assisted Received Space Shift Keying Modulation

Nov 23, 2025

To address the limited signal routing flexibility in single-RIS indoor systems caused by static reflection, this paper proposes a cooperative dual-reconfigurable intelligent surface (RIS) architecture. The first RIS provides baseline channel enhancement, while the second RIS (RIS₂) dynamically adjusts its reflection phases according to source data bits—enabling bit-driven physical-layer spatial modulation and beam-level signal routing. This work pioneers the integration of spatial shift keying (SSK) with dynamic phase mapping across two RISs, establishing an end-to-end transmission framework under a multi-hop channel model. Experimental results demonstrate significant improvements in achievable capacity and substantial reduction in outage probability across varying carrier frequencies and inter-RIS distances, thereby enabling high-accuracy, data-dependent intelligent indoor wireless coverage.

0 citationsRead paper

Quantum Voting Protocol for Centralized and Distributed Voting Based on Phase-Flip Counting

Oct 16, 2025

This paper addresses the challenges of anonymity, prevention of duplicate voting, and efficient tallying in quantum voting. We propose a dual-mode quantum voting protocol based on phase-flip counting, compatible with both centralized and distributed settings. Methodologically, we construct entangled candidate states using Hadamard and controlled-Z gates; votes are encoded via controlled-phase operations, and direct tallying is achieved through measurement of these candidate states—bypassing classical iterative aggregation. An entanglement-based verification mechanism is introduced to enhance security in remote voting. Our key contributions are: (i) the first integration of phase-flip encoding with multipartite entanglement to simultaneously guarantee voter anonymity and vote uniqueness; and (ii) support for quantum-parallel tallying, significantly improving scalability and efficiency in large-scale elections. The protocol is validated in scenarios with 4 voters/2 candidates and 8 voters/3 candidates, rigorously satisfying probability conservation, unbiased tallying, and cryptographic security requirements.

0 citationsRead paper