Precise positioning is a key factor and enabler technology for many use cases on intelligent transportation systems (ITS) and connected and automated vehicles (CAVs). Recently, the quantum positioning system (QPS) based on quantum ranging has emerged as a novel way to improve security and precision. As a key process of QPS, the entangled photons based ranging technology has picosecond-level clock synchronization, and the ranging accuracy can reach the Heisenberg limit. If promising QPS is deployed in the ITS and CAVs, it will cause a profound change. However, the existing QPS still lacks accuracy and robustness in different scenarios. To solve this problem, we proposed an efficient and robust fusion positioning system based on entangled photons. In this system, we derive the ranging accuracy limit with many factors and propose a fast data grouping and selection algorithm to improve real-time performance. Furthermore, we propose a fusion extended fingerprint localization method for robust positioning in the dynamic environment. The effectiveness and robustness of the system are verified by extensive experiments. When the range is 15m, the ranging accuracy can be limited to 0.0018m. The proposed system achieves the probability of positioning errors 90% within 0.13m with only two APs.
Precise positioning is a key factor and enabler technology for many use cases on intelligent transportation systems (ITS) and connected and automated vehicles (CAVs). Recently, the quantum positioning system (QPS) based on quantum ranging has emerged as a novel way to improve security and precision. As a key process of QPS, the entangled photons based ranging technology has picosecond-level clock synchronization, and the ranging accuracy can reach the Heisenberg limit. If promising QPS is deployed in the ITS and CAVs, it will cause a profound change. However, the existing QPS still lacks accuracy and robustness in different scenarios. To solve this problem, we proposed an efficient and robust fusion positioning system based on entangled photons. In this system, we derive the ranging accuracy limit with many factors and propose a fast data grouping and selection algorithm to improve real-time performance. Furthermore, we propose a fusion extended fingerprint localization method for robust positioning in the dynamic environment. The effectiveness and robustness of the system are verified by extensive experiments. When the range is 15m, the ranging accuracy can be limited to 0.0018m. The proposed system achieves the probability of positioning errors 90% within 0.13m with only two APs.
Recently, ranging has played an increasingly important role in many scenarios, but the problems of limited security and accuracy have always been troubling. The entanglement ranging method provides picosecond-level clock synchronization and unconditional security and is deemed a good solution to meet the growing demand for accuracy and security, promising to play a significant role in the next generation of navigation. This paper focuses on the ranging system based on entangled photons, and proposes solutions to the challenges of accuracy and robustness. Specifically, a coincidence counting calibration algorithm is proposed to suppress the background noise, and a novel data selection algorithm based on relative quantum efficiency is proposed to suppress abnormal coincidence counting. Then, to improve the ranging accuracy even in challenging scenarios, the paper proposed a novel density-based hierarchical filtering algorithm to detect and filter outliers, resulting in more precise and reliable ranging. Finally, extensive experiments are carried out on the built test platform to verify the safety and effectiveness of the proposed system. The results show that the ranging error is 0.0225 m in the test time of 1 ns. The proposed algorithm reduced the ranging error of abnormal data from 1.5205 m to 0.0100 m, and achieved the probability (90%) of ranging error within 0.0150 m.
A wide variety of positioning and ranging procedures are based on repeatedly sending electromagnetic pulses through space and measuring their time of arrival. This paper shows that quantum entanglement and squeezing can be employed to overcome the classical power/bandwidth limits on these procedures, enhancing their accuracy by a factor of the square root of the number of pulses employed times the number of photons per pulse. Frequency entangled pulses could be used to construct quantum positioning systems (QPS), to perform clock synchronization, or to do ranging (quantum radar): all of these techniques exhibit a similar enhancement compared with analogous protocols that use classical light.
The quantum positioning system (QPS) takes advantage of the entanglement property of the quantum system itself, and it is possible to breakthrough the limitation of the positioning accuracy due to signal bandwidth and power limitation in classical radio-based global positioning system (GPS), and improve the positioning performance. In this paper, a comprehensive survey of the research status of QPS and its several key components are presented, including the quantum entanglement states preparation, the quantum satellite technique, the optical signal acquisition, tracking and pointing (ATP) system, and the quantum positioning and clock synchronization technique. We also discuss the current challenges and future research directions at the end of the paper.
Intelligent transportation and autonomous driving require accurate and secure ranging. Quantum entanglement-based ranging provides physical-layer security and picosecond-level synchronization but remains vulnerable to signal sparsity and noise in dynamic environments. We propose a predictive reconstruction-based entangled photon ranging system that improves robustness and accuracy under low signal-to-noise ratio conditions. The system combines adaptive signal preparation, feature-aware processing, and high-fidelity sequence reconstruction through a triple-stream dual-channel differential-sequence network (TDDN) that extracts distribution features from photon arrival sequences. A coincidence-counting model incorporating atmospheric effects is also developed. Across 100 test subsets, the TDDN achieves a mean absolute error of 2.311 cm, a mean absolute percentage error of 2.976%, and a Pearson correlation coefficient of 0.99, reducing the mean absolute error by 18.0%–56.1% relative to multilayer perceptron, gated recurrent unit, long short-term memory, and recurrent neural network baselines under low signal-to-noise ratio conditions. These results demonstrate the potential of the proposed TDDN for robust and scalable ranging in safety-critical applications.
A quantum positioning system (QPS) is proposed that can provide a user with all four of his space-time coordinates. The user must carry a corner cube reflector, a good clock, and have a two-way classical channel of communication with the origin of the reference frame. Four pairs of entangled photons (biphotons) are sent through four interferometers: three interferometers are used to determine the user's spatial position, and an additional interferometer is used to synchronize the user's clock to coordinate time in the reference frame. The spatial positioning part of the QPS is similar to a classical time-of-arrival (TOA) system, however, a classical TOA system (such as GPS) must have synchronized clocks that keep coordinate time and therefore the clocks must have long-term stability, whereas in the QPS only a photon coincidence counter is needed and the clocks need only have short-term stability. Several scenarios are considered for a QPS: one is a terrestrial system and another is a space-based-system composed of low-Earth orbit (LEO) satellites. Calculations indicate that for a space-based system, neglecting atmospheric effects, a position accuracy below the 1 cm-level is possible for much of the region near the Earth. The QPS may be used as a primary system to define a global 4-dimensional reference frame.
The fusion of quantum computing and robotics represents a frontier in computational intelligence, promising to overcome the limitations of classical sensor fusion methods in precision, scalability, and real-time adaptability. This study proposes a hybrid quantum–classical framework designed to enhance the integration and synchronization of LiDAR, visual, and inertial sensor data, ultimately improving robotic perception and spatial awareness in complex environments. The model employs quantum-assisted optimization techniques to handle high-dimensional uncertainty, noise propagation, and data redundancy challenges inherent in multi-sensor processing. By leveraging variational quantum circuits and classical machine learning optimizers, the hybrid model achieves efficient data correlation and error minimization during sensor alignment. Benchmark experiments were conducted to evaluate the efficiency and precision of the proposed quantum-assisted sensor fusion system relative to conventional data integration algorithms. The findings reveal that hybrid quantum–classical systems yield substantial improvements in localization accuracy, temporal synchronization, and resilience to sensor noise, while maintaining computational feasibility within near-term quantum devices. This work highlights the potential of quantum-enhanced perception frameworks to accelerate the next generation of autonomous robotics, providing a foundation for adaptive control, intelligent navigation, and mission-critical decision-making under uncertainty.