In large-scale heterogeneous communication networks, routing optimization involves complex demands such as high-dimensional state spaces, multiple constraints, and real-time dynamics. A quantum-classical hybrid computing paradigm, leveraging quantum superposition and entanglement, offers a promising pathway to efficiently address NP-hard routing problems. By analyzing representative routing scenarios, a modeling framework for quantum algorithms was outlined, and key technical obstacles in applying quantum approaches to network routing were systematically summarized. These include conflicts between structural constraints and model expressiveness, biases in multi-objective formulations and delayed responses, the trade-off between diversity and convergence in high-dimensional solution spaces, and limited generalization capability under dynamic conditions. To overcome these issues, several directions were proposed: algorithmic expressiveness was enhanced through improved structural modeling, evolutionary design, and integrated scheduling strategies; computational accuracy and responsiveness were improved via multi-objective optimization, modular decoupling, and tight quantum-classical interaction; convergence and diversity were balanced through hybrid architectural design, evolutionary policy adaptation, and structural constraint management; and adaptability and generalization in dynamic environments were boosted by incorporating meta-learning, task decomposition, and structural transfer techniques. Furthermore, the co-development of quantum algorithms and hardware architectures were advocated, paving the way for practical deployment of quantum computing in communication networks within the NISQ era.
In large-scale heterogeneous communication networks, routing optimization involves complex demands such as high-dimensional state spaces, multiple constraints, and real-time dynamics. A quantum-classical hybrid computing paradigm, leveraging quantum superposition and entanglement, offers a promising pathway to efficiently address NP-hard routing problems. By analyzing representative routing scenarios, a modeling framework for quantum algorithms was outlined, and key technical obstacles in applying quantum approaches to network routing were systematically summarized. These include conflicts between structural constraints and model expressiveness, biases in multi-objective formulations and delayed responses, the trade-off between diversity and convergence in high-dimensional solution spaces, and limited generalization capability under dynamic conditions. To overcome these issues, several directions were proposed: algorithmic expressiveness was enhanced through improved structural modeling, evolutionary design, and integrated scheduling strategies; computational accuracy and responsiveness were improved via multi-objective optimization, modular decoupling, and tight quantum-classical interaction; convergence and diversity were balanced through hybrid architectural design, evolutionary policy adaptation, and structural constraint management; and adaptability and generalization in dynamic environments were boosted by incorporating meta-learning, task decomposition, and structural transfer techniques. Furthermore, the co-development of quantum algorithms and hardware architectures were advocated, paving the way for practical deployment of quantum computing in communication networks within the NISQ era.
Large-scale network optimization problems, prevalent in areas like telecommunications, logistics, and data center management, often present formidable challenges for classical optimization algorithms. The computational complexity associated with these problems frequently leads to unacceptable processing times. This research investigates the application of quantum-inspired algorithms to address these challenges. Specifically, we explore techniques mirroring quantum phenomena – superposition and interference – to accelerate the optimization process. The core idea involves representing multiple potential solutions concurrently through superposition and selectively amplifying promising solutions via interference. We present a novel algorithm design framework based on these principles, demonstrating its potential to achieve significant speedups compared to traditional methods. The theoretical analysis and illustrative examples highlight the advantages of this approach, offering a promising avenue for tackling complex optimization problems in large-scale networks. We evaluate the algorithm's performance on a simplified network routing problem, showcasing its ability to converge faster and potentially find better solutions. Further research will focus on adapting the algorithm for more complex network topologies and incorporating real-world constraints.
Quantum computers acting as ML accelerators have attracted considerable interest as a promising avenue for addressing the limitations of classical computing confronted with solving complex, high-dimensional problems and handling large data sets. The most exciting aspect of quantum computing lies in its quantum advantage via techniques such as quantum parallelism, superposition, and entanglement which allows not only for rapid data processing, but also a means to resolve complex optimization problems beyond the capacity of classical algorithms. Nevertheless, the usage of quantum computing in machine learning is still a fledgling domain, with challenges concerning quantum noise, hardware restrictions, and algorithmic development. In particular, this research is concerned with the development of and study on hybrid quantum-classical algorithms that target to improve ML models in terms of computational capabilities by making use of the best of both worlds. To address this, researchers are developing hybrid algorithms that leverage the strengths of quantum computing for specific tasks, like optimization and pattern recognition, while still relying on classical computing for more flexible and larger-scale processing, ultimately achieving more efficient machine learning systems that can handle large datasets and complex problem-solving more effectively. It includes potential hybrid approaches like quantum-enhanced optimization algorithms, quantum neural networks, and quantum support vector machines, and examines how such algorithms can be used for practical ML problems including data classification, regression and clustering. Furthermore, the paper proposes that hybrid quantum algorithms offer several advantages over their classical counterparts, such as faster convergence rates, higher accuracy in high-dimensional spaces, and improved handling of non-linear and complex problem landscapes. Key advancements in the area of data processing speed and efficiency in problem-solving with and without quantum in the machine learning workflow are also highlighted in the research through comparative analysis and simulation studies. Lastly, the paper concludes by outlining the obstacles yet to be surmounted and the opportunities that lie ahead for hybrid quantum-classical algorithms, such as further improvement of quantum hardware and error mitigation methods, as well as the design of new algorithmic paradigms that seamlessly integrates the classical and quantum computing spaces.
The increasing scale and interconnectedness of modern computational systems have intensified the demand for efficient algorithms capable of analyzing complex networks and solving graph-based optimization problems. Classical optimization techniques, while effective for moderate-sized datasets, often encounter exponential computational growth when applied to large-scale graph structures characterized by high dimensionality and dynamic topology. This paper explores quantum algorithmic frameworks for graph-based optimization and network analysis in large-scale computational systems, emphasizing the potential of quantum computing paradigms to address combinatorial complexity and enhance computational scalability. By synthesizing developments in quantum search algorithms, variational quantum circuits, and hybrid quantum–classical optimization methods, the study evaluates their applicability to tasks such as shortest-path discovery, clustering, community detection, and network resilience assessment. The findings suggest that quantum-enabled frameworks may provide complementary advantages in exploring vast solution spaces and improving optimization efficiency, particularly when integrated with classical preprocessing and orchestration strategies.
The rapidly increasing number of heterogeneous data ecosystems, such as structured databases, semi-structured data lakes, real-time streams, distributed knowledge graphs, has further complicated the issue of query planning and optimization in modern information systems extensively. The optimization of queries is essentially an NP-hard combinatorial problem especially when based on multi-join queries, distributed execution plan, dynamic cost model, and uncertainty in the data access patterns. Even decades of concept and engineering advances, classical database optimizers are rapidly becoming unable to offer a globally optimal execution plan when faced with high-dimensional data, high data volatility, with a very tight latency constraint. Good news Recent breakthroughs in quantum computing propose promising computational paradigms that can be used to solve combinatorial optimization problems via quantum parallelism, superposition, and entanglement. Nevertheless, even the near-term quantum hardware is limited in the number of available qubits, noise, and decoherence, making it impractical to achieve fully quantum database systems in any perceived future. This is also the reason why quantum-classical hybrid algorithms are being developed: designed to add quantum optimization subroutines to classical database architectures is guaranteed without breaking their compatibility with existing execution engines. The following paper presents a framework of scalable quantum-classical hybrid computing of complex query planning in heterogeneous data ecosystems. The framework breaks down query optimization into classical and quantum-amenable optimization, which allows NP-hard subproblems, such as join ordering, operator placement, and cost minimization, to be transformed into quantum optimization models such as Quadratic Unconstrained Binary Optimization (QUBO) and Ising Hamiltonians. Classical elements handle query parsing, semantic analysis, constraint verification, and orchestration, whereas quantum processors are called selectively to solve combinatorial decision-making tasks. The suggested architecture will consist of a layered execution, hybrid execution, cost-model encoding approach, and adaptive fallback mechanisms to make them robust to quantum hardware variability. This has been experimentally tested with simulated quantum annealing and variational quantum algorithms that exhibit a quantifiable improvement in the quality and scalability of optimization and heuristics based on entirely classical algorithms, especially in high-dimensional query plans. The findings confirm the practicability of quantum query optimization and put down a feasible route to implement quantum algorithms in the serving machine of the next-generation data management systems.
Quantum computing is a new discipline that uses the ideas of quantum physics to do calculations that are not possible with conventional computers. Quantum bits, called qubits, could exist in superposition states, making them suitable for parallel processing in contrast to traditional bits. When it comes to addressing complex challenges like proof simulation, optimization, and cryptography, quantum entanglement and quantum interference provide exponential improvements. This survey focuses on recent advances in entanglement routing, quantum key distribution (QKD), and qubit management for short- and long-distance quantum communication. It studies optimization approaches such as integer programming, reinforcement learning, and collaborative methods, evaluating their efficacy in terms of throughput, scalability, and fairness. Despite improvements, challenges remain in dynamic network adaptation, resource limits, and error correction. Addressing these difficulties necessitates the creation of hybrid quantum–classical algorithms for efficient resource allocation, hardware-aware designs to improve real-world deployment, and fault-tolerant architecture. Therefore, this survey suggests that future research focus on integrating quantum networks with existing classical infrastructure to improve security, dependability, and mainstream acceptance. This connection has significance for applications that require secure communication, financial transactions, and critical infrastructure protection.
The increasing demands for ultra-reliable low-latency communication (URLLC), high-capacity, connectivity, and mobility in next-generation wireless networks have driven the integration of unmanned aerial vehicles (UAVs) as flexible and adaptive communication nodes. UAV-assisted networks offer enhanced coverage, dynamic deployment, and cost-effective infrastructure for the sixth generation (6G) and beyond. However, optimizing UAV trajectory planning, resource allocation, and interference management is particularly challenging, especially in dynamic and large-scale environments. Classical reinforcement learning (RL) techniques, while effective in adaptive optimization, suffer from computational inefficiencies and slow convergence in high-dimensional state-action spaces. To address these constraints, quantum reinforcement learning (QRL) emerges as a novel paradigm that leverages quantum computing principles, such as superposition and entanglement, to enhance decision-making efficiency. This study provides a comprehensive overview of QRL algorithms and investigates their transformative potential in UAV-assisted wireless communication networks. We analyze UAV deployment strategies, network architectures, and topologies, highlighting their advantages as well as inherent challenges. Furthermore, we examine the limitations of classical RL algorithms and assess how QRL overcomes them while improving computational efficiency and exploration capabilities. In addition, we present a detailed review of existing QRL algorithms applicable to next-generation UAV-enhanced wireless networks, identifying key challenges that must be addressed for practical deployment. Finally, we identify open research challenges and offer future directions for integrating QRL into UAV-assisted communication, paving the way for scalable, intelligent, and high-performance wireless systems.