The complexity of large-scale 6G-and-beyond networks demands innovative approaches for multi-objective optimization over vast search spaces, a task often intractable. Quantum computing (QC) emerges as a promising technology for efficient large-scale optimization. We present our vision of leveraging QC to tackle key classes of problems in future mobile networks. By analyzing and identifying common features, particularly their graph-centric representation, we propose a unified strategy involving QC algorithms. Specifically, we outline a methodology for optimization using quantum annealing as well as quantum reinforcement learning. Additionally, we discuss the main challenges that QC algorithms and hardware must overcome to effectively optimize future networks.
The complexity of large-scale 6G-and-beyond networks demands innovative approaches for multi-objective optimization over vast search spaces, a task often intractable. Quantum computing (QC) emerges as a promising technology for efficient large-scale optimization. We present our vision of leveraging QC to tackle key classes of problems in future mobile networks. By analyzing and identifying common features, particularly their graph-centric representation, we propose a unified strategy involving QC algorithms. Specifically, we outline a methodology for optimization using quantum annealing as well as quantum reinforcement learning. Additionally, we discuss the main challenges that QC algorithms and hardware must overcome to effectively optimize future networks.
This paper explores the application of quantum annealing to the challenging domain of combinatorial optimization within large-scale networks. Specifically, it investigates the feasibility of utilizing quantum annealing for problems such as network routing and scheduling, where traditional classical algorithms often struggle due to computational complexity. The core approach involves formulating the network optimization problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem. This transformation enables the mapping of the problem onto the quantum annealing hardware, leveraging the quantum annealing process – characterized by its adiabatic evolution – to discover the optimal solution. We argue that this method presents a potential advantage over conventional algorithms for large-scale network scenarios, offering a pathway to tackling problems previously considered intractable. The theoretical framework and underlying principles are thoroughly discussed, outlining the methodology and highlighting the anticipated benefits.
Abstract Sixth Generation (6G) communication networks are anticipated to advance the fifth generation technologies as well as introduce new revolutionary technologies to deliver aggressive data rates, end-to-end latency, reliability, energy efficiency, security, network capacity, and mobility requirements of the future innovative applications. To deliver these demands, ultra-fast and secure computing capability for processing a huge amount of data, application-related tasks, and resource management algorithms will be required. Many 6G applications will use advanced learning techniques that work on large data sets and their working can be made more efficient with the help of Quantum Computing (QC) instead of conventional classical computing. In this paper, we provide an overview of QC and 6G networks and highlight the benefit of integrating these two technologies. We briefly review the recent work and present a framework for QC-assisted 6G networks. We also discuss four use cases of QC in 6G networks, including quick task offloading, fast optimization algorithm results, efficient machine learning techniques, and improved security. Finally, we discuss several open challenges of using QC in the context of 6G network applications.
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.
This paper investigates the application of quantum annealing to address the challenges of combinatorial optimization within large-scale network routing. Traditional classical algorithms frequently encounter significant computational bottlenecks when attempting to determine optimal routes across complex networks, often failing to scale effectively with increasing network size and complexity. Quantum annealing offers a potentially transformative approach by leveraging quantum mechanical effects to explore solution spaces more efficiently. This work outlines a methodology for mapping network routing problems – characterized by a vast number of possible routes – onto the quantum annealing hardware. We present a model for representing the problem, formulating it as an Ising model, and then detail the annealing process. The core claim is that quantum annealing can provide a viable solution for optimizing network routes, particularly in scenarios where classical methods are computationally intractable. We explore the underlying principles of quantum annealing, focusing on its ability to escape local minima and achieve a lower energy state, representing the optimal solution. The research aims to demonstrate the feasibility and potential advantages of employing quantum annealing in practical network routing applications, paving the way for more efficient and robust network designs.
Quantum computing and artificial intelligence (AI) are positioned as groundbreaking tools capable of tackling complexities inherent to modern computation posed by high-dimensional optimization problems.This research presents a quantum-enhanced optimization framework that leverages the QAOA (Quantum Approximate Optimization Algorithm) and VQE (Variational Quantum Eigen solver) within a hybrid quantum-classical paradigm.Machine learning models, including reinforcement learning and NN (neural networks), are integrated to optimize the quantum circuit parameters, enhance convergence, and improve scalability for complex problem spaces.Experimental evaluations demonstrate that this approach outperforms traditional optimization techniques in terms of efficiency and solution accuracy, particularly for large-scale multi-objective tasks.The framework is applied to key domains such as smart energy systems, logistics, and scientific modeling, illustrating its versatility and alignment with sustainable development goals.This study also explores the limitations of current quantum hardware and discusses future pathways for advancing hybrid quantum AI systems, offering significant contributions to next generation computational methodologies and intelligent problem solving.
Quantum computing has emerged as a transformative technology, particularly for solving optimization problems, which are notoriously difficult for classical computers. Quantum algorithms, leveraging quantum parallelism and entanglement, promise significant speedups for a wide range of optimization tasks, from combinatorial optimization to machine learning. This paper reviews recent advancements in quantum algorithms for optimization problems, highlighting key algorithms such as Grover’s search algorithm, the Quantum Approximate Optimization Algorithm (QAOA), and the Quantum Alternating Operator Ansatz (QAOA). We also explore the challenges and potential of quantum computing in addressing real-world optimization problems, such as in logistics, finance, and machine learning. The paper discusses the current state of quantum hardware, as well as theoretical and practical limitations in implementing these quantum algorithms. Finally, we propose future research directions to overcome existing challenges and enhance the application of quantum algorithms in solving complex optimization problems