Quantum-Classical Adaptive Navigation: QAOA-PPO Framework for GNSS-Denied Urban Environments
DOI:
https://doi.org/10.4108/eetiot.11077Keywords:
Autonomous vehicles, GNSS-denied navigation, NIST validation, quantum approximate optimization algorithm, reinforcement learning, urban positioningAbstract
This study shows a mixed quantum-classical guidance system that combines an eight-layer Quantum Approximate Optimization Algorithm (QAOA) with Proximal Policy Optimization (PPO) reinforcement learning for cities that do not have GNSS. The design changes the settings of the quantum circuit on the fly based on real-time data from the environment. This makes noise in cities less of a static mistake source and more of an optimization constraint. After testing on 47.3 kilometers of GNSS-denied paths in three cities using a three-tier DARPA QuANET-aligned protocol and NIST-traceable equipment, the results show that the average positional accuracy is 0.15 ± 0.03 meters, which is 70% better than high-grade GPS/INS systems. With adaptive correction, the framework keeps 98.2% of its coherence under ISO 16750-3 shaking and 95.3% of its operational reliability during 24-hour signal rejection. It also reduces drift by 82% compared to LiDAR-SLAM over a kilometer. It is statistically significant (F (3,1196) =87.3, p<0.001, ·²=0.38), and the effect sizes are big. The system passes SAE Level 4 power limits (45.2W), goes beyond DARPA QuANET 2025 goals, and gets Hybrid Readiness Level 7 approval. Each unit is expected to cost $210,000, and it creates the first policy-ready quantum navigation platform for smart city application.
Downloads
References
[1] X. Song, H. He, Z. Zeng, Y. Peng, and W. Hong, "Performance evaluation method for GNSS/INS integrated navigation system," Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024), vol. 13506, pp. 198–210, 2025, doi: https://doi.org/10.1117/12.3057564.
[2] P. Wang, Y. Gao, Q. Zhao, Y. Wang, F. Zhou, and D. Zhang, "An Enhanced, Real-Time, Low-Cost GNSS/INS Integrated Navigation Algorithm and Its Platform Design," Sensors (Basel, Switzerland), vol. 25, no. 7, p. 2119, 2025, doi: https://doi.org/10.3390/s25072119.
[3] O. Elmaghraby, P. R. M. Araujo, S. I. K. Abdelaziz, and A. Noureldin, "Comparative Study of Traditional and Deep Learning Feature Detectors and Matchers for Land Vehicle Monocular Visual Odometry," 2025 IEEE International systems Conference (SysCon), pp. 1–8, 7–10 April 2025 2025, doi: https://doi.org/10.1109/SysCon64521.2025.11014855.
[4] J. Shang, Y. Liu, Y. Xu, J. Xiao, and D. Ma, "Robust Global Localization for Urban Autonomous Vehicles via 3D Geometric-Enhanced Visual Place Recognition," IEEE Transactions on Intelligent Transportation Systems, pp. 1–15, 2025, doi: https://doi.org/10.1109/TITS.2025.3590549.
[5] C. Ban, L. Wang, R. Chi, T. Su, and Y. Ma, "A Camera-LiDAR-IMU fusion method for real-time extraction of navigation line between maize field rows," Computers and Electronics in Agriculture, vol. 223, p. 109114, 2024/08/01/ 2024, doi: https://doi.org/10.1016/j.compag.2024.109114.
[6] M. Stefanoni, I. Kovács, P. Sarcevic, and Á. Odry, "A Survey on the Main Techniques Adopted in Indoor and Outdoor Localization," Electronics, vol. 14, no. 10, p. 2069, 2025, doi: https://doi.org/10.3390/electronics14102069.
[7] J. Bernard et al., "Atom interferometry using σ+-σ− Raman transitions between| F= 1, m F=∓ 1〉 and| F= 2, m F=±1〉," Physical Review A, vol. 105, no. 3, p. 033318, 2022, doi: https://doi.org/10.1103/PhysRevA.105.033318.
[8] X.-L. Chen et al., "Robust large-momentum-transfer atom interferometry with Raman adiabatic rapid passage," Optics Communications, vol. 573, p. 131005, 2024/12/15/ 2024, doi: https://doi.org/10.1016/j.optcom.2024.131005.
[9] L. Rossi, M. Reguzzoni, Ö. Koç, G. Rosi, and F. Migliaccio, "Assessment of gravity field recovery from a quantum satellite mission with atomic clocks and cold atom gradiometers," Quantum Science and Technology, vol. 8, no. 1, p. 014009, 2022, doi: https://doi.org/10.1088/2058-9565/aca8cc.
[10] H. P. Paudel, G. R. Lander, S. E. Crawford, and Y. Duan, "Sensing at the Nanoscale Using Nitrogen-Vacancy Centers in Diamond: A Model for a Quantum Pressure Sensor," Nanomaterials, vol. 14, no. 8, p. 675, 2024, doi: https://doi.org/10.3390/nano14080675.
[11] R. Katsumi, K. Takada, F. Jelezko, and T. Yatsui, "Recent progress in hybrid diamond photonics for quantum information processing and sensing," Communications Engineering, vol. 4, no. 1, p. 85, 2025/05/08 2025, doi: https://doi.org/10.1038/s44172-025-00398-2.
[12] Z. K. Qiao et al., "Feasibility Study on Using Atomic Gravimeters for Detecting Urban Underground Spaces," IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1–11, 2025, doi: https://doi.org/10.1109/TIM.2025.3565251.
[13] B. Stray et al., "Quantum gravity gradiometry for future mass change science," EPJ Quantum Technology, vol. 12, no. 1, p. 35, 2025, doi: https://doi.org/10.1140/epjqt/s40507-025-00338-1.
[14] J. Chapman, Z. C. Seskir, S. Belguith, and T. Tryfonas, "Quantum Sensing Technologies and Smart Cities: Opportunities and Challenges," 2024 IEEE International Smart Cities Conference (ISC2), pp. 1–6, 29 Oct.–1 Nov. 2024 2024, doi: https://doi.org/10.1109/ISC260477.2024.11004264.
[15] R. Fakhimi and H. Validi, "Quantum Approximate Optimization Algorithm (QAOA)," Encyclopedia of Optimization, pp. 1–7, 2020, doi: https://doi.org/10.1007/978-3-030-54621-2_854-1.
[16] D. Volpe, G. Orlandi, and G. Turvani, "Improving the Solving of Optimization Problems: A Comprehensive Review of Quantum Approaches," Quantum Reports, vol. 7, no. 1, p. 3, 2025, doi: https://doi.org/10.3390/quantum7010003.
[17] M. W. Geda and Y. M. Tang, "Adaptive hybrid quantum-classical computing framework for deep space exploration mission applications," Journal of Industrial Information Integration, vol. 44, p. 100803, 2025/03/01/ 2025, doi: https://doi.org/10.1016/j.jii.2025.100803.
[18] J. Zhang and J. Zhang, "Classical Dance-Metaheuristic: a metaheuristic optimization algorithm inspired by classical dance," Control Systems and Optimization Letters, vol. 3, no. 2, pp. 165–173, 2025, doi: https://doi.org/10.59247/csol.v3i2.206.
[19] R. Ghanbarzadeh and S. Mirjalili, "A structured review of large language models in metaheuristic optimisation," Decision Analytics Journal, vol. 15, p. 100587, 2025/06/01/ 2025, doi: https://doi.org/10.1016/j.dajour.2025.100587.
[20] T. Stuyver and C. W. Coley, "Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability," The Journal of Chemical Physics, vol. 156, no. 8, 2022, doi: https://doi.org/10.1063/5.0079574.
[21] M. Chiofalo, "Quantum Toolbox for Neurobiology Sensory Systems," Journal of Physics: Conference Series, vol. 2948, no. 1, p. 012015, 2025, doi: https://doi.org/10.1088/1742-6596/2948/1/012015.
[22] M. AbuGhanem, "IBM quantum computers: evolution, performance, and future directions," The Journal of Supercomputing, vol. 81, no. 5, p. 687, 2025/04/01 2025, doi: https://doi.org/10.1007/s11227-025-07047-7.
[23] V. Braun and V. Clarke, "Conceptual and design thinking for thematic analysis," Qualitative psychology, vol. 9, no. 1, p. 3, 2022, doi: https://psycnet.apa.org/doi/10.1037/qup0000196.
[24] A. Rajak, S. Suzuki, A. Dutta, and B. K. Chakrabarti, "Quantum annealing: An overview," Philosophical Transactions of the Royal Society A, vol. 381, no. 2241, p. 20210417, 2023, doi: https://doi.org/10.1098/rsta.2021.0417.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Gabriel Silva Atencio
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
