Quantum Computing Research in the Arab World

Quantum computing (QC) is one of the most transformative scientific and technological advances of the 21^st century, introducing entirely new paradigms for solving computational problems that have long been considered intractable for classical systems. By using the principles of quantum mechanics—superposition, entanglement, and interference—QC has the potential to tackle challenges in fields such as optimization, cryptography, materials science, artificial intelligence, and many others, offering solutions that go beyond the capabilities of conventional computing frameworks. Though the field is still in its developmental stages, progress is being made worldwide, expanding its scope and potential impact.^2,13 As QC matures, researchers are working to address its challenges, such as mitigating noise in quantum systems, improving the scalability of qubit architectures, defining and exploring the quantum advantages, and refining algorithms to exploit quantum principles effectively. These efforts are not confined to a few regions; they reflect a global endeavor with significant contributions emerging from diverse regions worldwide. Notably, the Arab world has become increasingly active in the QC landscape, with multiple countries investing in QC research, fostering collaborations and developing talent to engage with this transformative technology.

This article samples specific contributions to QC research from the Arab world, recognizing that other institutions and research groups across the region are exploring a range of topics including quantum machine learning (QML),³ localization, optimization algorithms for finance, hardware development, quantum simulations, and cybersecurity applications. We aim to provide an overview and a snapshot of ongoing efforts and challenges by focusing on QML and QC for location-tracking and spatial systems.

Quantum Machine Learning

QML merges the groundbreaking principles of quantum mechanics with the adaptive frameworks of machine learning, enabling innovative approaches to data processing and deeper exploration of complex problem solving, particularly in scenarios involving high-dimensional quantum data that challenge classical modeling techniques, complex optimization landscapes often associated with NP-hard problems, and growing demands for privacy-preserving and security-resilient solutions in sensitive application domains. The Center for Quantum and Topological Systems (CQTS) at New York University Abu Dhabi, driven by the eBRAIN Lab Quantum Team, together with the Quantum Physics and Spintronics Team at Hassan II University of Casablanca, are actively shaping this evolving field, driving forward pragmatic and secure strategies to address fundamental challenges with creativity and purpose (see figure).

Figure.  A comprehensive overview of QML and QFL, along with their challenges and applications, outlining quantum technologies’ current state and potential in addressing complex and multidimensional problems.

Early efforts aimed at improving parameterized quantum circuits—by adapting classical parameter initialization methods—have reduced the risk of barren plateaus, a phenomenon where gradients vanish with increasing circuit depth or system size, thereby facilitating more effective optimization of quantum neural networks (QNNs).¹² Building on these stability enhancements, our attention has shifted toward collaborative learning paradigms, such as quantum federated learning (QFL), where multiple stakeholders (for example, healthcare institutions or genomic research facilities) can refine QML models jointly without sharing sensitive data. By synchronizing model parameters rather than sharing sensitive inputs, QFL safeguards patient privacy and proprietary information without violating strict regulatory frameworks. Beyond preserving confidentiality, QFL promotes a more diverse and inclusive research ecosystem, encouraging entities with different data modalities in model learning.⁸

Alongside these foundational developments, QML has shown promise in more specialized applications. For instance, in quantum state tomography, applying QML techniques can reduce the measurement overhead required to reconstruct large-scale quantum systems.¹¹ This reduction in complexity makes state characterization more practical, facilitating more accessible experimentation and potentially accelerating the deployment of advanced quantum technologies. Similarly, hybrid quantum-classical methods using variational techniques have emerged as resource-efficient tools for estimating ground-state energies and molecular properties,⁷ thus accelerating materials discovery. In the financial sector, combining QML with graph-based representations of transactional data helps uncover subtle patterns indicative of fraud,¹⁰ while ongoing refinements to quantum support vector machines and other classifiers highlight the value of incremental accuracy improvements.⁹ Furthermore, incorporating Grover’s algorithm into quantum perceptrons exemplifies how carefully chosen quantum subroutines can enhance classification performance across diverse benchmarks.⁶ Recent work on quanvolutional neural networks (QuNNs) explores their potential in quantum cybersecurity,⁴ addressing their vulnerability to adversarial inputs by developing techniques to boost the resilience of QuNN, demonstrating that these models can provide enhanced robustness against attacks under specific scenarios.

Collectively, these research initiatives shape a coherent trajectory that guides QML from its emerging foundations toward a progressively adaptable, context-sensitive framework capable of tackling diverse practical challenges. Maintaining a balanced perspective throughout these conjugated attempts shows that QML is positioned not as a competitor to classical approaches but rather as a complementary set of techniques. Through the adoption of collaborative learning, robust architectures, and thoughtfully designed quantum algorithms, the field is advancing toward unlocking the potential of QML in healthcare, finance, materials science, and cybersecurity.

Quantum Computing for Location-Tracking and Spatial Systems

The Wireless Research Center at the American University in Cairo has been contributing to advancing QC applications, particularly in the context of localization and optimization problems. Their work emphasizes practical implementations of quantum algorithms and their integration with real-world systems.

In this regard, the vision in Shokry and Youssef¹⁴ highlights that while QC holds promise for revolutionizing location-tracking and spatial systems—in terms of more efficiency in space and running time—there are a number of challenges that need to be addressed, from theoretical research to practical implementations. In particular, the vision lists a number of challenges related to algorithms, hardware, and integration. The paper also discusses practical issues, such as the constraints imposed by near-term quantum hardware, where QC platforms have a limited number of noisy qubits.

Building on this vision, Shokry and Youssef^16,17 introduced quantum algorithms for calculating the similarity between received signal strength (RSS) vectors from different signal sources, for example, WiFi access points (APs), using different metrics. This is a common functionality in widely used fingerprinting-based localization.^1,5,19,20,21 The presented quantum algorithms provide exponential savings in storage requirements and running time compared with their classical counterpart, while maintaining the same accuracy. In a follow-up work,²³ the group further supports high-accuracy, low-latency worldwide positioning envisioned for next-generation cellular positioning. By entangling the test RSS vector with the fingerprint RSS vectors, the proposed quantum algorithm has a sub-linear complexity, which is exponentially better than even the previous quantum fingerprint positioning systems.

To address the practicality of the near-term quantum devices, the group analyzed the practical implementation and deployment issues of the performance of the quantum positioning algorithms on IBM QC cloud machines. These effects include gate and measurement errors, highlighting interesting practical implementations.¹⁵ Addressing the same limitations of near-term quantum devices, the system in Zook et al.²² proposes a quantum algorithm for accurate multi-story building localization. The method leverages binary neurons, which use fewer qubits compared with traditional quantum neurons, enabling efficient deployment on near-term quantum devices. The approach demonstrated exponential savings in computational resources while maintaining high localization accuracy.

Another interesting dimension of QC research in location-tracking systems is leveraging quantum annealing for optimizing location-tracking–related problems. Specifically, in another paper Shokry and Youssef¹⁸ tackle the optimization problem of selecting the minimum number of APs in a large-scale localization system required to achieve a particular localization accuracy. The proposed quantum algorithm uses a quadratic unconstrained binary optimization (QUBO) framework to maximize the importance of selected APs while minimizing redundancy, validated on D-Wave quantum annealers. This method ensures robust location estimation while reducing computational complexity, enhancing the practicality of quantum-enhanced localization systems.

This thread of work highlights the ongoing work in the region addressing computational bottlenecks in location-tracking and spatial systems using QC, bridging theoretical advances with practical applications.

Conclusion

QC holds transformative potential for various computing challenges, with applications in finance, healthcare, cybersecurity, and many other areas. The Arab world has a growing interest in QC and its applications. Here, we highlighted a sample of the QC research going on in the region in QML and the application of QC for location-tracking and spatial systems. The provided algorithms show the potential gain of using QC to solve real-world problems, while handling practical short-term constraints.

With sustained commitment and strategic planning in QC research, the region has the potential to contribute meaningfully to the advancement of this emerging area, driving innovation and economic growth in the coming decades.

Acknowledgment

This work was supported in part by the NYUAD Center for Quantum and Topological Systems (CQTS), funded by Tamkeen under the NYUAD Research Institute grant CG008, and the Center for Cyber Security (CCS), funded by Tamkeen under the NYUAD Research Institute Award G1104.