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.
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.
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.
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.
This paper presents a novel adaptive algorithm based on quantum entanglement, designed to significantly enhance the efficiency and robustness of computations. Traditional algorithms often struggle with the inherent limitations of classical systems, particularly in scenarios demanding precise and reliable results. This work explores the utilization of quantum entanglement as a key mechanism to overcome these limitations. The algorithm leverages entangled states to enable parallel processing, minimize errors, and achieve a higher degree of accuracy. We detail the core principles of the algorithm, the mathematical foundations underpinning its operation, and the potential benefits in terms of computational speed and stability. The paper concludes with a discussion of the challenges and future directions for research in this area.
As one of the state-of-the-art technologies currently being studied in the field of target positioning, the entangled light quantum-based passive target positioning method uses quantum entangled states to detect and localize the target, thus providing highly-accurate and anti-interference positioning services. The conventional methods seldom consider the problem that maybe a pseudo-target is detected when there is actually no target due to the interference of particles, scatterers, and noise photons during light quantum transmission. To address this problem, this paper proposes a new passive target positioning method on uncertain detection path based on entangled light quantum. Specifically, first of all, by converting the continuous light energy into the discrete photon number, the photon return rate is used to calculate the number of arrival photons based on the counting characteristics of the single photon detector. Second, the detection probabilities of signal, idler, and noise photons are calculated to obtain the coincidence probability at the receiver. Third, according to the coincidence count value of the entangled light time pulse sequence, the Constant False Alarm Rate (CFAR) detection model is established, and the corresponding detection threshold is adaptively adjusted with the change of the background noise to achieve highly-sensitive target detection. Finally, the relative error for each light source is considered to select the optimal light sources with the smallest relative distance errors for target positioning. In addition, the results show that the detection probability of the proposed method is over 95% with the false alarm probability 0.05, the positioning error is centimeter-level, and the confidence probability within 2.60 cm can reach 92%.
Aiming at the problem that the dynamic change of the scattering environment affects the propagation distance estimation performance by different light sources, resulting in low positioning accuracy and poor robustness, a light quantum positioning method based on integrated coincidence counting and adaptive light source selection is proposed. Specifically, the method considers the dynamic changes of the scattering environment to group the entangled light time pulse sequences, calculates the signal propagation distance of each light source under each group based on the coincidence count, and takes the relative error of each light source under each group as the basis to dynamically select different light sources for positioning. The experimental results show that the proposed method has higher positioning accuracy and stronger positioning robustness.
Precise clock synchronization is important for quantum networking, enhanced position, navigation, timing, and other applications that require clock synchronization better than GPS, but remains an ongoing challenge. The authors propose a solution using hardware native to quantum networking. This quantum-time-transfer technique can quickly achieve picosecond-level clock synchronization despite relatively low-performance quantum-photon sources and detection equipment. Furthermore, it is robust against the high loss and high noise channel conditions representative of daytime space-Earth links, and could provide high-precision secure timing in GPS-denied environments.