Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2409.00792 · International Conference on Indoor Positioning and Indoor Navigation

An Efficient Quantum Binary-Neuron Algorithm for Accurate Multi-Story Floor Localization

Yousef Zook, Ahmed Shokry, Moustafa A. Youssef

Published Oct 1, 2024• 3 citations
arXivHTMLPDF

Abstract

Accurate floor localization in a multi-story environment is an important but challenging task. Among the current floor localization techniques, fingerprinting is the mainstream technology due to its accuracy in noisy environments. To achieve accurate floor localization in a building with many floors, we have to collect sufficient data on each floor, which needs significant storage and running time; preventing fingerprinting techniques from scaling to support large multi-story buildings, especially on a worldwide scale.In this paper, we propose a quantum algorithm for accurate multi-story localization. The proposed algorithm leverages quantum computing concepts to provide an exponential enhancement in both space and running time compared to the classical counterparts. In addition, it builds on an efficient binary-neuron implementation that can be implemented using fewer qubits compared to the typical non-binary neurons, allowing for easier deployment with near-term quantum devices. We implement the proposed algorithm on a real IBM quantum machine and evaluate it on three real indoor testbeds. Results confirm the exponential saving in both time and space for the proposed quantum algorithm, while keeping the same localization accuracy compared to the traditional classical techniques, and using half the number of qubits required for other quantum localization algorithms.

DOI
10.1109/ipin62893.2024.10786146

Related papers

QLoc: A Realistic Quantum Fingerprint-based Algorithm for Large Scale Localization

Ahmed Shokry, Moustafa A. Youssef

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.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 1, 2022•11 citations

A Quantum Algorithm for RF-based Fingerprinting Localization Systems

Ahmed Shokry, Moustafa A. Youssef

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.

Conference on Local Computer Networks•Aug 26, 2022•18 citations

An Efficient Quantum Euclidean Similarity Algorithm for Worldwide Localization

Ahmed Shokry, Moustafa A. Youssef

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.

arXiv:2407.14680•Jul 19, 2024•1 citation

A quantum algorithm for localization using limited cellular information

Ahmed Shokry, Moustafa A. Youssef

Fingerprinting cellular localization offers high accuracy but is constrained by the current devices’ limited access to neighboring towers, often crucial for precise localization. In this paper, we introduce a practical quantum algorithm for scalable single cell tower localization. The proposed algorithm encodes both the user’s recent cell tower connections IDs (online sequence) and the reference database (offline sequences) into quantum states, enabling efficient quantum search to find the reference location that best matches the user’s current cell tower observations. We present a complete system design, address key implementation challenges, and evaluate our algorithm in a real testbed. Implementation results on the IBM quantum machine simulator demonstrate that our quantum algorithm matches the classical localization accuracy while providing exponential saving in space and quadratic speedup. This highlights the promise of the proposed algorithm for large-scale cellular localization.

npj Wireless Technology•May 4, 2026•0 citations

Fingerprinting-Based Indoor Localization With Hybrid Quantum-Deep Neural Network

Paulson Eberechukwu Numan, Minsoo Jeong, Hyunwoo Park +2 more

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.

IEEE Access•Jan 1, 2023•10 citations

A Quantum Fingerprinting Algorithm for Next Generation Cellular Positioning

Yousef Zook, Ahmed Shokry, Moustafa A. Youssef

The recent release of the third-generation partnership project, Release 17, calls for sub-meter cellular positioning accuracy with reduced latency in the calculation. To provide such high accuracy on a worldwide scale, leveraging the received signal strength (RSS) for positioning promises ubiquitous availability in the current and future equipment. RSS Fingerprint-based techniques have shown great potential for providing high accuracy in both indoor and outdoor environments. However, fingerprint-based positioning faces the challenge of providing a fast matching algorithm that can scale worldwide. In this paper, we propose a cosine similarity-based quantum algorithm for enabling fingerprint-based high accuracy and worldwide positioning that can be integrated with the next generation of 5G and 6G networks and beyond. By entangling the test RSS vector with the fingerprint RSS vectors, the proposed quantum algorithm has a complexity that is exponentially better than its classical version as well as the state-of-the-art quantum fingerprint positioning systems, both in the storage space and the running time. We implement the proposed quantum algorithm and evaluate it in a cellular testbed on a real IBM quantum machine. Results show the exponential saving in both time and space for the proposed quantum algorithm while keeping the same positioning accuracy compared to the traditional classical fingerprinting techniques and the state-of-the-art quantum algorithms.

arXiv:2306.08108•IEEE Journal on Selected Areas in Communications•Oct 9, 2023•15 citations
Accurate floor localization in a multi-story environment is an important but challenging task. Among the current floor localization techniques, fingerprinting is the mainstream technology due to its accuracy in noisy environments. To achieve accurate floor localization in a building with many floors, we have to collect sufficient data on each floor, which needs significant storage and running time; preventing fingerprinting techniques from scaling to support large multi-story buildings, especially on a worldwide scale.In this paper, we propose a quantum algorithm for accurate multi-story localization. The proposed algorithm leverages quantum computing concepts to provide an exponential enhancement in both space and running time compared to the classical counterparts. In addition, it builds on an efficient binary-neuron implementation that can be implemented using fewer qubits compared to the typical non-binary neurons, allowing for easier deployment with near-term quantum devices. We implement the proposed algorithm on a real IBM quantum machine and evaluate it on three real indoor testbeds. Results confirm the exponential saving in both time and space for the proposed quantum algorithm, while keeping the same localization accuracy compared to the traditional classical techniques, and using half the number of qubits required for other quantum localization algorithms.
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.
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.
Fingerprinting cellular localization offers high accuracy but is constrained by the current devices’ limited access to neighboring towers, often crucial for precise localization. In this paper, we introduce a practical quantum algorithm for scalable single cell tower localization. The proposed algorithm encodes both the user’s recent cell tower connections IDs (online sequence) and the reference database (offline sequences) into quantum states, enabling efficient quantum search to find the reference location that best matches the user’s current cell tower observations. We present a complete system design, address key implementation challenges, and evaluate our algorithm in a real testbed. Implementation results on the IBM quantum machine simulator demonstrate that our quantum algorithm matches the classical localization accuracy while providing exponential saving in space and quadratic speedup. This highlights the promise of the proposed algorithm for large-scale cellular localization.
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 recent release of the third-generation partnership project, Release 17, calls for sub-meter cellular positioning accuracy with reduced latency in the calculation. To provide such high accuracy on a worldwide scale, leveraging the received signal strength (RSS) for positioning promises ubiquitous availability in the current and future equipment. RSS Fingerprint-based techniques have shown great potential for providing high accuracy in both indoor and outdoor environments. However, fingerprint-based positioning faces the challenge of providing a fast matching algorithm that can scale worldwide. In this paper, we propose a cosine similarity-based quantum algorithm for enabling fingerprint-based high accuracy and worldwide positioning that can be integrated with the next generation of 5G and 6G networks and beyond. By entangling the test RSS vector with the fingerprint RSS vectors, the proposed quantum algorithm has a complexity that is exponentially better than its classical version as well as the state-of-the-art quantum fingerprint positioning systems, both in the storage space and the running time. We implement the proposed quantum algorithm and evaluate it in a cellular testbed on a real IBM quantum machine. Results show the exponential saving in both time and space for the proposed quantum algorithm while keeping the same positioning accuracy compared to the traditional classical fingerprinting techniques and the state-of-the-art quantum algorithms.