Hybrid quantum–classical signal processing has emerged as a promising paradigm for overcoming the computational limitations of conventional algorithms in analyzing high-dimensional data generated by modern smart instrumentation systems. This paper presents a novel hybrid quantum–classical signal processing framework designed for efficient feature extraction from complex, multidimensional sensor signals encountered in industrial monitoring, biomedical diagnostics, environmental sensing, and intelligent manufacturing. The proposed architecture integrates quantum variational circuits with classical deep learning models to exploit quantum superposition and entanglement for enhanced feature representation while maintaining the robustness and scalability of classical optimization techniques. Initially, raw sensor signals are preprocessed through adaptive filtering and normalization before being encoded into quantum states using amplitude encoding. Quantum feature maps and parameterized quantum circuits generate discriminative latent representations, which are subsequently refined by classical neural networks for accurate pattern recognition and decision-making. Extensive simulation studies demonstrate that the proposed framework achieves superior feature extraction capability, reduced computational complexity for high-dimensional datasets, and improved classification accuracy compared with conventional signal processing methods. Furthermore, the hybrid architecture exhibits enhanced robustness against measurement noise and varying signal conditions, making it suitable for real-time intelligent instrumentation applications. The integration of quantum computing with advanced signal processing establishes a scalable pathway toward next-generation smart sensing systems capable of supporting Industry 5.0, autonomous cyber-physical systems, and AI-enabled instrumentation. The proposed methodology offers a practical foundation for future quantum-enhanced signal analytics in high-performance intelligent measurement and monitoring environments.

Keywords - Hybrid Quantum-Classical Signal Processing; High-Dimensional Feature Extraction; Smart Instrumentation; Quantum Machine Learning; Intelligent Sensor Systems; Signal Analytics.

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1. Yuan Y, Yu JL, Pan Y, Zheng S. Low-resolution phase shifter-based hybrid precoding for mmWave massive MIMO. EURASIP Journal on Wireless Communications and Networking. 2024 Nov 5;2024(1):85.
2. Zeng J, Liao B. Transmit and receive hybrid beamforming design for OFDM dual-function radar- communication systems. EURASIP journal on advances in signal processing. 2023 Mar 20;2023(1):37.
3. Maeng SJ, Yapici Y, Güvenç I, Dai H, Bhuyan A. Hybrid precoding for mmWave massive MIMO with one-bit DAC. IEEE Communications Letters. 2020 Aug 10;24(12):2941-5.
4. Morsali A, Haghighat A, Champagne B. Deep learning-based hybrid analog-digital signal processing in mmWave massive-MIMO systems. IEEE Access. 2022 Jul 5;10:72348-62.
5. Kassam J, Miri M, Magueta R, Castanheira D, Pedrosa P, Silva A, Dinis R, Gameiro A. Two-step multiuser equalization for hybrid mmwave massive mimo gfdm systems. Electronics. 2020 Jul 29;9(8):1220.
6. Xie H, Wang Y, Gao Z, Ganthia BP, Truong CV. Research on frequency parameter detection of frequency shifted track circuit based on nonlinear algorithm. Nonlinear Engineering. 2021 Jan 1;10(1):592-9.
7. Gu J, Wang W, Yin R, Truong CV, Ganthia BP. Complex circuit simulation and nonlinear characteristics analysis of GaN power switching device. Nonlinear Engineering. 2021 Jan 1;10(1):555- 62.
8. Rubavathy SJ, Venkatasubramanian R, Kumar MM, Ganthia BP, Kumar JS, Hemachandu P, Ramkumar MS. Smart grid based multiagent system in transmission sector. In2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) 2021 Sep 2 (pp. 1-5). IEEE.
9. Priyadarshini L, Kundu S, Maharana MK, Ganthia BP. Controller design for the pitch control of an autonomous underwater vehicle. Engineering, Technology & Applied Science Research. 2022 Aug 7;12(4):8967-71.
10. Zheng W, Mehbodniya A, Neware R, Wawale SG, Ganthia BP, Shabaz M. Modular unmanned aerial vehicle platform design: Multi-objective evolutionary system method. Computers and Electrical Engineering. 2022 Apr 1;99:107838.
11. Ganthia BP, Praveen BM. A comprehensive study of various multi-area hybrid power systems for generation control. Int J Electr Power Mach Syst. 2025;3(2):28-42.
12. Guo S, Li X, Guo Z, Zhao X, Meng S, Li Z. Polarization-independent optoelectronic modulator based on graphene ridge structure. Nanomaterials. 2021 Sep 29;11(10):2559.
13. Deng X, Li Z, Cao F, Hong E, Fang X. Woven fibrous photodetectors for scalable UV optical communication device. Advanced Functional Materials. 2023 Jun;33(23):2213334.
14. Guo FH, Hao LZ, Yu WZ, Li SQ, Liu GC, Hao JY, Liu YJ. High‐performance Si/VO2‐nanorod heterojunction photodetector based on photothermoelectric effect for detecting human radiation. Rare Metals. 2024 Mar;43(3):1177-85.
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16. Sahoo LK, Mahanta S, Kumar S, Kaliappan S, Ganthia BP. Fast Transient Detection and Power Quality Improvement in Grid-Connected Photovoltaic Systems Using Spiking Neural Networks. In2026 International Conference on Electrical and Electronics for Sustainable Innovations (ICEESI) 2026 Mar 27 (pp. 1-6). IEEE.
17. Mahanta S, Sahoo LK, Balaraju V, Kaliappan S, Ganthia BP. Design and Performance Analysis of a Distributed Modular Active Power Filter Network for Power Quality Improvement in PV System. In2026 International Conference on Electrical and Electronics for Sustainable Innovations (ICEESI) 2026 Mar 27 (pp. 1-8). IEEE.
18. Ganthia BP, Barik SK, Nayak B, Priyadarshi N, Padmanaban S, Hiran KK, Holm-Nielson JB, Bansal RC. 2 Power control of modified type III DFIG-based wind turbine system using four-mode type I fuzzy logic controller. Artificial Intelligence and Internet of Things for Renewable Energy Systems. 2021 Nov 22;12:41.

Hybrid quantum–classical signal processing has emerged as a promising paradigm for overcoming the computational limitations of conventional algorithms in analyzing high-dimensional data generated by modern smart instrumentation systems. This paper presents a novel hybrid quantum–classical signal processing framework designed for efficient feature extraction from complex, multidimensional sensor signals encountered in industrial monitoring, biomedical diagnostics, environmental sensing, and intelligent manufacturing. The proposed architecture integrates quantum variational circuits with classical deep learning models to exploit quantum superposition and entanglement for enhanced feature representation while maintaining the robustness and scalability of classical optimization techniques. Initially, raw sensor signals are preprocessed through adaptive filtering and normalization before being encoded into quantum states using amplitude encoding. Quantum feature maps and parameterized quantum circuits generate discriminative latent representations, which are subsequently refined by classical neural networks for accurate pattern recognition and decision-making. Extensive simulation studies demonstrate that the proposed framework achieves superior feature extraction capability, reduced computational complexity for high-dimensional datasets, and improved classification accuracy compared with conventional signal processing methods. Furthermore, the hybrid architecture exhibits enhanced robustness against measurement noise and varying signal conditions, making it suitable for real-time intelligent instrumentation applications. The integration of quantum computing with advanced signal processing establishes a scalable pathway toward next-generation smart sensing systems capable of supporting Industry 5.0, autonomous cyber-physical systems, and AI-enabled instrumentation. The proposed methodology offers a practical foundation for future quantum-enhanced signal analytics in high-performance intelligent measurement and monitoring environments.

Keywords - Hybrid Quantum-Classical Signal Processing; High-Dimensional Feature Extraction; Smart Instrumentation; Quantum Machine Learning; Intelligent Sensor Systems; Signal Analytics.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Yuan Y, Yu JL, Pan Y, Zheng S. Low-resolution phase shifter-based hybrid precoding for mmWave massive MIMO. EURASIP Journal on Wireless Communications and Networking. 2024 Nov 5;2024(1):85.
2. Zeng J, Liao B. Transmit and receive hybrid beamforming design for OFDM dual-function radar- communication systems. EURASIP journal on advances in signal processing. 2023 Mar 20;2023(1):37.
3. Maeng SJ, Yapici Y, Güvenç I, Dai H, Bhuyan A. Hybrid precoding for mmWave massive MIMO with one-bit DAC. IEEE Communications Letters. 2020 Aug 10;24(12):2941-5.
4. Morsali A, Haghighat A, Champagne B. Deep learning-based hybrid analog-digital signal processing in mmWave massive-MIMO systems. IEEE Access. 2022 Jul 5;10:72348-62.
5. Kassam J, Miri M, Magueta R, Castanheira D, Pedrosa P, Silva A, Dinis R, Gameiro A. Two-step multiuser equalization for hybrid mmwave massive mimo gfdm systems. Electronics. 2020 Jul 29;9(8):1220.
6. Xie H, Wang Y, Gao Z, Ganthia BP, Truong CV. Research on frequency parameter detection of frequency shifted track circuit based on nonlinear algorithm. Nonlinear Engineering. 2021 Jan 1;10(1):592-9.
7. Gu J, Wang W, Yin R, Truong CV, Ganthia BP. Complex circuit simulation and nonlinear characteristics analysis of GaN power switching device. Nonlinear Engineering. 2021 Jan 1;10(1):555- 62.
8. Rubavathy SJ, Venkatasubramanian R, Kumar MM, Ganthia BP, Kumar JS, Hemachandu P, Ramkumar MS. Smart grid based multiagent system in transmission sector. In2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) 2021 Sep 2 (pp. 1-5). IEEE.
9. Priyadarshini L, Kundu S, Maharana MK, Ganthia BP. Controller design for the pitch control of an autonomous underwater vehicle. Engineering, Technology & Applied Science Research. 2022 Aug 7;12(4):8967-71.
10. Zheng W, Mehbodniya A, Neware R, Wawale SG, Ganthia BP, Shabaz M. Modular unmanned aerial vehicle platform design: Multi-objective evolutionary system method. Computers and Electrical Engineering. 2022 Apr 1;99:107838.
11. Ganthia BP, Praveen BM. A comprehensive study of various multi-area hybrid power systems for generation control. Int J Electr Power Mach Syst. 2025;3(2):28-42.
12. Guo S, Li X, Guo Z, Zhao X, Meng S, Li Z. Polarization-independent optoelectronic modulator based on graphene ridge structure. Nanomaterials. 2021 Sep 29;11(10):2559.
13. Deng X, Li Z, Cao F, Hong E, Fang X. Woven fibrous photodetectors for scalable UV optical communication device. Advanced Functional Materials. 2023 Jun;33(23):2213334.
14. Guo FH, Hao LZ, Yu WZ, Li SQ, Liu GC, Hao JY, Liu YJ. High‐performance Si/VO2‐nanorod heterojunction photodetector based on photothermoelectric effect for detecting human radiation. Rare Metals. 2024 Mar;43(3):1177-85.
15. Pradhan RR, Mantry K, Pradhan S, Parida S, Ganthia BP. Harmonic elimination in photovoltaic (PV) systems using various multilevel inverter topologies. Trends Electr Eng. 2026;16(1):-
16. Sahoo LK, Mahanta S, Kumar S, Kaliappan S, Ganthia BP. Fast Transient Detection and Power Quality Improvement in Grid-Connected Photovoltaic Systems Using Spiking Neural Networks. In2026 International Conference on Electrical and Electronics for Sustainable Innovations (ICEESI) 2026 Mar 27 (pp. 1-6). IEEE.
17. Mahanta S, Sahoo LK, Balaraju V, Kaliappan S, Ganthia BP. Design and Performance Analysis of a Distributed Modular Active Power Filter Network for Power Quality Improvement in PV System. In2026 International Conference on Electrical and Electronics for Sustainable Innovations (ICEESI) 2026 Mar 27 (pp. 1-8). IEEE.
18. Ganthia BP, Barik SK, Nayak B, Priyadarshi N, Padmanaban S, Hiran KK, Holm-Nielson JB, Bansal RC. 2 Power control of modified type III DFIG-based wind turbine system using four-mode type I fuzzy logic controller. Artificial Intelligence and Internet of Things for Renewable Energy Systems. 2021 Nov 22;12:41.