WiFi RSSI fingerprinting is a cost-effective indoor localization technique leveraging existing infrastructure, but it suffers from time-varying signal fluctuations that degrade accuracy. This paper proposes a quantum-enhanced fingerprinting approach using a Swap Test for cosine similarity and a quantum oracle that encodes offline fingerprints in superposition for parallel comparison with online measurements. To mitigate RSSI noise, we introduce a PCA-based preprocessing that projects RSSI data into a lower-dimensional subspace, maximizing the angular separation of fingerprints and improving classification. Experiments on a Qiskit simulator demonstrate that our quantum fingerprinting method achieves improved localization accuracy as the number of measurement shots increases, leveraging quantum parallelism. To our knowledge, this is the first detailed implementation of quantum fingerprinting for indoor localization in Qiskit, laying the foundation for quantum-based positioning.
WiFi RSSI fingerprinting is a cost-effective indoor localization technique leveraging existing infrastructure, but it suffers from time-varying signal fluctuations that degrade accuracy. This paper proposes a quantum-enhanced fingerprinting approach using a Swap Test for cosine similarity and a quantum oracle that encodes offline fingerprints in superposition for parallel comparison with online measurements. To mitigate RSSI noise, we introduce a PCA-based preprocessing that projects RSSI data into a lower-dimensional subspace, maximizing the angular separation of fingerprints and improving classification. Experiments on a Qiskit simulator demonstrate that our quantum fingerprinting method achieves improved localization accuracy as the number of measurement shots increases, leveraging quantum parallelism. To our knowledge, this is the first detailed implementation of quantum fingerprinting for indoor localization in Qiskit, laying the foundation for quantum-based positioning.
Fingerprinting is one of the most commonly used techniques for indoor localization due to their high accuracy in the presence of the wireless channel noise. Nonetheless, they require significant storage overhead and running time. This becomes even more important when scaling such systems to a worldwide scale.Quantum computing can speedup computation of intractable problems that are hard to solve on the traditional classical computers. In this paper, we propose a realistic quantum fingerprint based algorithm that builds on quantum computing concepts to scale. In particular, the proposed fingerprinting algorithm can provide exponential enhancement in both space and running time over the classical fingerprinting techniques. We explain the details of the quantum fingerprint construction, how to encode the fingerprint signal strength vectors in quantum states, and how to calculate the quantum similarity between the online signal strength measurement and the offline ones. In addition, we study the different practical implementation and deployment issues on IBM quantum computing cloud machines.Implementation and evaluation of our algorithm in a real testbed using quantum machines confirms its ability to correctly obtain the estimated location with an exponential enhancement in both time and space compared to the traditional classical fingerprinting techniques. This highlights the promise of our algorithm to scale fingerprinting localization to be used globally.
This paper proposes a hybrid quantum neural network (HQNN) for indoor user localization using received signal strength indicator (RSSI) values. We use publicly available RSSI datasets for indoor localization using WiFi, Bluetooth, and Zigbee to test the performance of the proposed HQNN. We also compare the performance of the HQNN with the recently proposed quantum fingerprinting-based user localization method. Our results show that the proposed HQNN performs better than the quantum fingerprinting algorithm since the HQNN has trainable parameters in the quantum circuits, whereas the quantum fingerprinting algorithm uses a fixed quantum circuit to calculate the similarity between the test data point and the fingerprint dataset. Unlike prior works, we also test the performance of the HQNN and quantum fingerprint algorithm on a real IBM quantum computer using cloud quantum computing services. Therefore, this paper examines the performance of the HQNN on noisy intermediate scale (NISQ) quantum devices using real-world RSSI localization datasets. The novelty of our approach lies in the use of simple feature maps and ansatz with fewer neurons, alongside testing on actual quantum hardware using real-world data, demonstrating practical applicability in real-world scenarios.
Fingerprinting is one of the mainstream technologies for localization. However, it needs significant storage overhead and running time, preventing it from scaling to support world-wide indoor/outdoor localization.Quantum computing has the potential to revolutionize computation by making some classically intractable problems solvable on quantum computers. In this paper, we propose a quantum fingerprint-based localization algorithm for enabling large-scale location tracking systems, envisioning future era of location tracking and spatial systems. Specifically, we propose a quantum algorithm that provides an exponential enhancement of both the space and running time complexity compared to the traditional classical systems. We give the details of how to build the quantum fingerprint, how to encode the received signal strength (RSS) measurements in quantum particles, and finally; present a quantum algorithm for calculating the cosine similarity between the online RSS measurements and the fingerprint ones.Results from deploying our algorithm in three real testbeds on IBM Quantum Experience machines confirm the ability of our quantum system to get the same accuracy as the classical one but with the potential exponential saving in both space and running time.
Fingerprinting techniques are widely used for localization because of their accuracy, especially in the presence of wireless channel noise. However, the fingerprinting techniques require significant storage and running time, which is a concern when implementing such systems on a global worldwide scale. In this paper, we propose an efficient quantum Euclidean similarity algorithm for wireless localization systems. The proposed quantum algorithm offers exponentially improved complexity compared to its classical counterpart and even the state-of-the-art quantum localization systems, in terms of both storage space and running time. The basic idea is to entangle the test received signal strength (RSS) vector with the fingerprint vectors at different locations and perform the similarity calculation in parallel to all fingerprint locations. We give the details of how to construct the quantum fingerprint, how to encode the RSS measurements in quantum particles, and finally; present the quantum algorithm for calculating the Euclidean similarity between the online RSS measurements and the fingerprint ones. Implementation and evaluation of our algorithm in a real testbed using a real IBM quantum machine as well as a simulation for a larger testbed confirm its ability to correctly obtain the estimated location with an exponential enhancement in both time and space compared to the traditional classical fingerprinting techniques and the state-of-the-art quantum localization techniques.
This paper presents an approach for enhancing indoor localization accuracy using a hybrid quantum deep neural network model (H-QDNN). To improve the accuracy of indoor localization based on contemporary techniques, we employ the combined strengths of quantum computing (QC) and deep neural networks (DNN). The strengths of QC, which accelerates the training process and enables efficient handling of complex data representations through quantum superposition and entanglement, were combined with DNN, known for its ability to extract meaningful features and learn complex patterns from data. The proposed model can be trained using small datasets, reducing the need for extensive data, particularly useful in indoor localization, where data collection can be time-consuming and resource-intensive. To evaluate the effectiveness of our proposed approach, we conduct extensive experiments and comparisons with existing state-of-the-art methods. The results demonstrate that the H-QDNN model significantly improves indoor localization accuracy compared to traditional techniques. Additionally, we provide insights into the factors contributing to enhanced performance, such as the quantum-inspired algorithms utilized and the integration of mixed fingerprints.
The limitations of Global Positioning Systems (GPS) in indoor environments have led to the development of a number of indoor localization systems. One potential system is based on WiFi signals. Wifi-based Localization systems leverage Wireless Local Area Network (WLAN) fingerprinting to estimate user positions.This thesis explores a hybrid quantum-classical machine learning approach to improve wifi-based indoor localization systems, where quantum circuits extract features while classical models handle classification. The proposed quantum-classical hybrid system achieved 96.83% accuracy in noise-free conditions. The results are further compared with classical machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest (RF), to benchmark our hybrid model. Our model outperformed all these models, where KNN, SVM, and Random Forest achieved accuracy rates of 74.2%, 92.0%, and 95.4%, respectively. We also included other popular metrics and obtained promising results, where our model achieved precision of 97%, recall of 96.5%, and F1 score of 96.8%, outperforming all the classical models we benchmarked with. However, the hybrid model outperformed them. However, real-world quantum systems face noise-related challenges, such as depolarizing errors, bit-flip errors, and decoherence, which degrade performance. Therefore, we adopted a number of popular noise-models and evaluated our proposed model accordingly. Our model showed resistance to noise, especially in low to moderate noise rates.The research demonstrates how quantum computing can surpass machine learning, opening new avenues for indoor navigation, human motion detection, and intrusion detection.