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.
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.
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.
Indoor localization using Received Signal Strength Indication (RSSI) is a notoriously challenging problem due to signal noise from multipath fading and interference. Although quantum machine learning (QML) presents a new paradigm for tackling such problems, the design of effective quantum feature maps remains a significant bottleneck. This paper introduces a novel approach for automated quantum feature map design using a Hierarchical Genetic Algorithm (HGA). We propose a sequential Hybrid Quantum-Classical Neural Network (HQNN) architecture where the initial quantum feature map is not handcrafted but is evolved by the HGA. The chromosome for the GA represents a hierarchical sequence of parametric and nonparametric quantum gate layers, allowing for the discovery of complex data-encoding structures. We evaluated the performance of our approach on wireless indoor user localization datasets. The results are compared with a standard HQNN using a ‘ZZFeatureMap’ proposed in a prior work, demonstrating the potential and challenges of evolutionary quantum circuit design. Our findings indicate that while the HGA effectively explores a vast search space of quantum circuits and improves against the benchmark in majority of cases, achieving superior performance on sparse, high-interference datasets requires careful consideration of the fitness landscape and generalization capabilities of the evolved feature maps.
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.
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.
Quantum Machine Learning represents a promising approach to address complex computational challenges beyond classical computing capabilities. In this study, we investigate its application to healthcare, focusing specifically on the classification of cardiovascular diseases. Classical Machine Learning methods often struggle with high-dimensional data, resulting in limited accuracy and reduced generalization, especially in real-world medical scenarios. To overcome these issues, we propose a Hybrid Quantum-Neural Network that combines classical Autoencoders for dimensionality reduction with Quantum Neural Networks, facilitating effective data processing compatible with current NISQ hardware. Evaluated on the Cleveland dataset, our HQNN achieves state-of-the-art classification accuracy up to 90.98%, outperforming purely classical counterparts by capturing complex feature relationships in the quantum domain. We further demonstrate the effectiveness of the proposed HQNN model in scenarios characterized by limited training data and realistic noisy quantum environments, performing robustness analyses and simulations using IBM’s quantum hardware framework. These findings highlight the practical advantages of our Hybrid Quantum-Neural Network, underscoring its suitability and immediate applicability for real-world healthcare scenarios. The code is made publicly available.