Efficient end-to-end entanglement distribution in quantum information networks requires routing under limited resources and fidelity constraints. We study entanglement routing as a fidelity-constrained unsplittable multicommodity flow problem that maximizes the number of admitted requests. As a proof of concept, we integrate neutral-atom quantum optimization into a hybrid classical--quantum column-generation framework. A classical restricted master problem selects routes, while a pricing problem generates fidelity-feasible paths. We formulate this NP-hard constrained shortest-path pricing problem as a quadratic unconstrained binary optimization (QUBO) problem and address it with hardware-aware register embedding and instance-driven pulse shaping. To our knowledge, this is the first study of neutral-atom quantum optimization as a pricing oracle for fidelity-constrained entanglement routing. On small benchmark instances evaluated with neutral-atom processor emulators, the method, combined with warm-start and post-processing, achieves an optimality gap below 1% across all tested sizes; the selected simulated-annealing route-generation baseline exhibits gaps of up to 6%. These results indicate that the generated bitstrings can provide useful candidate routes for classical refinement within column generation. This proof of concept does not establish quantum utility or scalability; further evaluation on larger instances, against stronger classical baselines, and on quantum hardware is needed.
Efficient end-to-end entanglement distribution in quantum information networks requires routing under limited resources and fidelity constraints. We study entanglement routing as a fidelity-constrained unsplittable multicommodity flow problem that maximizes the number of admitted requests. As a proof of concept, we integrate neutral-atom quantum optimization into a hybrid classical--quantum column-generation framework. A classical restricted master problem selects routes, while a pricing problem generates fidelity-feasible paths. We formulate this NP-hard constrained shortest-path pricing problem as a quadratic unconstrained binary optimization (QUBO) problem and address it with hardware-aware register embedding and instance-driven pulse shaping. To our knowledge, this is the first study of neutral-atom quantum optimization as a pricing oracle for fidelity-constrained entanglement routing. On small benchmark instances evaluated with neutral-atom processor emulators, the method, combined with warm-start and post-processing, achieves an optimality gap below 1% across all tested sizes; the selected simulated-annealing route-generation baseline exhibits gaps of up to 6%. These results indicate that the generated bitstrings can provide useful candidate routes for classical refinement within column generation. This proof of concept does not establish quantum utility or scalability; further evaluation on larger instances, against stronger classical baselines, and on quantum hardware is needed.
This paper presents a hybrid classical-quantum approach to solve mixed-integer linear programming (MILP) using neutral-atom quantum computations. We apply Benders decomposition (BD) to segment MILPs into a master problem (MP) and a subproblem, where the MP is addressed using a neutral-atom device, after being transformed into a quadratic unconstrained binary optimization (QUBO) model, with an automatized procedure. Our MILP to QUBO conversion tightens the upper bounds of the continuous variables involved, positively impacting the required qubit count, and the convergence of the algorithm. To solve the QUBO, we develop a heuristic for atom register embedding and apply a variational algorithm for pulse shaping. In addition, we implement a proof of concept that outperforms existing solutions. We also conduct preliminary numerical results: In a series of small MILP instances our algorithm identifies over 95% of feasible solutions of high quality, outperforming classical BD approaches where the MP is solved using simulated annealing.
This paper evaluates quantum annealing as a potential method for solving multi-path routing in large-scale quantum communication networks. The routing problem was formulated as a quadratic unconstrained binary optimization (QUBO) problem that minimizes negative throughput while satisfying quantum network constraints. The experimental framework tests four solution approaches on randomly generated networks: (1) the classical quantum fidelity rate maximizing (Q-FiRM) algorithm, (2) classical simulated annealing, (3) D-Wave hybrid solver, and (4) pure quantum annealing on D-Wave Advantage2 hardware. Results show that while quantum annealers produce feasible solutions, the classical Q-FiRM algorithm achieves superior throughput and faster computation times for this problem class. Among QUBO approaches, hybrid solvers demonstrate computational advantages over pure classical simulated annealing while maintaining comparable solution quality. These findings provide practical insight into the current capabilities and limitations of quantum optimization for quantum internet infrastructure design.
Routing in wireless communication networks is shaped by mobility, interference, congestion, and competing service requirements, making route selection a high-dimensional constrained optimization problem rather than a simple shortest-path task. This paper investigates the use of hybrid classical--quantum methods for wireless routing, focusing on the Quantum Approximate Optimization Algorithm (QAOA) and quantum walks as candidate mechanisms for exploring complex routing spaces. The paper examines how wireless routing can be expressed as a constrained graph optimization problem in which routing objectives, flow constraints, connectivity requirements, and interference effects are mapped into quantum-compatible Hamiltonian representations. It then discusses how these approaches can be integrated into a hybrid architecture in which classical systems perform network monitoring, graph construction, pre-processing, and deployment, while quantum subroutines are used for selected optimization components. The analysis shows that the potential value of quantum routing lies primarily in the treatment of difficult combinatorial subproblems rather than end-to-end replacement of classical routing frameworks. The paper also highlights practical limitations arising from state preparation, constraint encoding, oracle construction, hardware noise, limited qubit resources, and hybrid execution overhead. It is argued that any meaningful near-term advantage will depend on careful problem decomposition, compact encoding, and tight classical--quantum integration.
Routing in wireless communication networks is shaped by mobility, interference, congestion, and competing service requirements, making route selection a high-dimensional constrained optimization problem rather than a simple shortest path task. This paper investigates the use of hybrid classical–quantum methods for wireless routing, focusing on the Quantum Approximate Optimization Algo-rithm (QAOA) and quantum walks as candidate mechanisms for exploring complex routing spaces. The paper examines how wireless routing can be expressed as a constrained graph optimization problem in which routing objectives, flow constraints, connectivity requirements, and interference effects are mapped into quantum-compatible Hamiltonian representations. It then discusses how these approaches can be integrated into a hybrid architecture in which classical systems perform network monitoring, graph construction, pre-processing, and deployment, while quantum subroutines are used for selected optimization components. The analysis shows that the potential value of quantum routing lies primarily in the treatment of difficult combinatorial subproblems rather than end-to-end replace-ment of classical routing frameworks. The paper also highlights practical limitations arising from state preparation, constraint encoding, oracle construction, hardware noise, limited qubit resources, and hybrid execution overhead. It is argued that any meaningful near-term advantage will depend on careful problem decomposition, compact encoding, and tight classical–quantum integration. DOI: https://doi.org/10.17762/ijisae.v14i1s.8149
The Energy Internet (EI) is a new concept aimed at enhancing the integration of renewable energy sources with the energy grid. Energy-efficient path selection in EI is NP-hard. This research presents an innovative Quadratic Unconstrained Binary Optimization (QUBO) and Ising Hamiltonian formulation for energy routing. The validation and scalability of the proposed formulation were evaluated by applying quantum-inspired annealing and quantum gate optimization to two case studies, a 9-node and a 30-node EI network. A comparative analysis was presented between classical optimization using the Dijkstra algorithm, optimization-based methods, and QAOA using the Qiskit Sampler Primitive, NumpyEigenSolver, and quantum-inspired annealing using the Ocean exact Solver, D-Wave Tabu Sampler, and D-Wave Simulated Annealing. Simulation results based on the proposed formulation agree with the exact solution, while the runtime of classical approaches is less than that of quantum approaches. However, the Simulated Annealing sampler offers the shortest runtime among all quantum methods.
Quadratic Unconstrained Binary Optimization (QUBO) problems are prevalent in real-world applications, such as portfolio optimization, but pose significant computational challenges for large-scale instances. We propose a hybrid quantum-classical framework that leverages neutral atom quantum computing to address QUBO problems by mapping them to the Maximum Weighted Independent Set (MWIS) problem on unit disk graphs. Our approach employs spatial grid partitioning to decompose the problem into manageable subgraphs, solves each subgraph using Analog Hamiltonian Simulation (AHS), and merges solutions greedily to approximate the global optimum. We evaluate the framework on a 50-asset portfolio optimization problem using historical S&P 500 data, benchmarking against classical simulated annealing. Results demonstrate competitive performance, highlighting the scalability and practical potential of our method in the Noisy Intermediate-Scale Quantum (NISQ) era. As neutral atom quantum hardware advances, our framework offers a promising path toward solving large-scale optimization problems efficiently.