This work benchmarks Quantum Annealing (QA), Hybrid Annealing (HA), and Simulated Annealing (SA) for solving Wavelength Allocation (WA) and Routing and Wavelength Allocation (RWA) problems in static Wavelength Division Multiplexing (WDM) optical networks. We introduce a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation for the static RWA problem, designed to minimize link utilization, distinguishing it from existing dynamic formulations.
This work benchmarks Quantum Annealing (QA), Hybrid Annealing (HA), and Simulated Annealing (SA) for solving Wavelength Allocation (WA) and Routing and Wavelength Allocation (RWA) problems in static Wavelength Division Multiplexing (WDM) optical networks. We introduce a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation for the static RWA problem, designed to minimize link utilization, distinguishing it from existing dynamic formulations.
We develop four different quadratic unconstrained binary optimization formulations of routing problems relevant to the optical transport layers in telecommunications networks. Our formulations handle joint routing and wavelength assignment, unicast, multicast trees, and shared risk avoidance. We show that this approach is viable, even for multicast, and we test and compare the effectiveness of each formulation at solving these problems using the DWave hybrid solver. We describe expedients we applied to ensure valid solutions, along with challenges of the QUBO approach.
A challenge for scalability of demand-responsive, elastic optical Dense Wavelength Division Multiplexing (DWDM) and Flexgrid networks is the computational complexity of allocating many optical routes on large networks. We demonstrate that demand satisfaction problems in communication networks can be formulated as quadratic unconstrained binary optimisation (QUBO) problems, and solved using a hybrid quantum annealer. Efficient encodings are developed which solve both unicast and multicast multicommodity-flow problems, while also adhering to individual requirements for maximum latency and resilience for each route. We present several QUBO formulations and analyse the qubit scaling. We demonstrate solutions using a hybrid solver, D-Wave Quantum Advantage QPU. Progress in generating optimal solutions with efficient use of computational resources will be beneficial to telecoms operators, enabling them to run dynamic optical network infrastructures which use resources efficiently, are resilient to local faults and cyber-attacks, and can be elastically responsive to demands.
Quantum annealing (QA) offers a physics-inspired approach for such subproblems by mapping them onto an Ising Hamiltonian, which is algebraically equivalent to a Quadratic Unconstrained Binary Optimization (QUBO) model executable on quantum annealers or hybrid solvers. However, their practical employment in wireless systems critically depends on the specific problem formulation, on the particular variable encoding, constraint handling, solver selection, hardware embedding, and fair benchmarking. This paper develops wireless-oriented QUBO modeling guidelines for QA-assisted resource allocation. We first present a beginner-friendly toy example in which a terrestrial-aerial resource-allocation problem with a fractional signal-to-interference-plus-noise ratio (SINR) maximization objective is reformulated into a two-bit QUBO via Boolean interpolation, converted into an Ising representation, and decoded back into a wireless scheduling decision. The same scheduling is applicable to UAV, aircraft, satellite, and terrestrial links in SAGIN systems. We then review the QA foundations and solver ecosystems, including binary quadratic, discrete quadratic, constrained quadratic, and hybrid models from the perspective of NG wireless optimization. Furthermore, we discuss both binary and discrete encoding, penalty-based constraint conversion, embedding overhead, solution decoding, and feasibility verification. A QUBO Compatibility Index is introduced as a lightweight diagnostic metric for assessing the associated QUBO formulation readiness, rather than a predictor of runtime or solution quality. Finally, a case study on max-min uplink power control in cell-free massive MIMO illustrates how a fairness-oriented wireless problem can be discretized, constrained, converted into the concept of a QUBO, and evaluated against classical baselines. A host of key challenges and future directions are also discussed.
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.
The objective of this paper is to introduce the application of quantum annealing (QA) to electromagnetic (EM) engineering. We demonstrate that numerous EM design and inverse problems naturally admit a mathematical formulation in terms of binary quadratic interactions. By utilizing didactic, simplified examples, we illustrate how this underlying physical structure allows for a direct mapping of EM problems onto Quadratic Unconstrained Binary Optimization (QUBO) models and equivalent Ising Hamiltonians. All numerical experiments are conducted using Simulated Annealing as a classical proxy, since the primary contribution of this work is the QUBO formulation itself rather than its execution on quantum hardware. Our results suggest that a broad class of EM problems—including array thinning, reconfigurable intelligent surface optimization, subarray partitioning, and electromagnetic inverse scattering—are suitable candidates for optimization using near-future quantum annealing architectures, once hardware connectivity and noise floors reach the required specifications.
Vehicle Routing Problem is a well-known optimization issue, which has many extensions resulting from practical appliances. The problem is NP-hard, therefore it may be impossible to find an optimal solution for big instances, with traditional computation methods. Recent development of quantum technology, may be a response for this challenge, which however requires a different formulation of the model. The problem is then modeled using Quadratic Unconstrained Binary Optimization (QUBO) equations. Such model can be run using quantum annealers (QA), designed specifically for optimization purposes. This paper is a systematic survey of Vehicle Routing Problem models that are formulated using QUBO, to be solved on quantum computers. The main goal of the paper is to check research gaps in this subject, compare and explain different approaches of modeling various problem extensions with QUBO. The results obtained in reviewed papers will be presented and analyzed. Moreover, the theoretical basis for quantum computation will be presented, with focus on D-Wave company solutions.