Agile networks with fast adaptation and reconfiguration capabilities are required for on-demand provisioning of various network services. We propose a new methodical framework for short-time network optimization based on quantum computing (QC) and integer linear program (ILP) models, which has the potential of realizing a real-time network automation. We define methods to map a nearly real-world ILP model for resource provisioning to a quadratic unconstrained binary optimization (QUBO) problem, which is solvable on quantum annealer (QA). We concentrate on the three-node network to evaluate our approach and its obtainable quality of solution using the state-of-the-art quantum annealer D-Wave Advantage™ 5.2/5.3. By studying the annealing process, we find annealing configuration parameters that obtain feasible solutions close to the reference solution generated by the classical ILP-solver CPLEX. Further, we studied the scaling of the network problem and provide estimations on quantum annealer's hardware requirements to enable a proper QUBO problem embedding of larger networks. We achieved the QUBO embedding of networks with up to 6 nodes on the D-Wave Advantage™. According to our estimates a real-sized network with 12 to 16 nodes require a QA hardware with at least 50000 qubits or more.
Agile networks with fast adaptation and reconfiguration capabilities are required for on-demand provisioning of various network services. We propose a new methodical framework for short-time network optimization based on quantum computing (QC) and integer linear program (ILP) models, which has the potential of realizing a real-time network automation. We define methods to map a nearly real-world ILP model for resource provisioning to a quadratic unconstrained binary optimization (QUBO) problem, which is solvable on quantum annealer (QA). We concentrate on the three-node network to evaluate our approach and its obtainable quality of solution using the state-of-the-art quantum annealer D-Wave Advantage™ 5.2/5.3. By studying the annealing process, we find annealing configuration parameters that obtain feasible solutions close to the reference solution generated by the classical ILP-solver CPLEX. Further, we studied the scaling of the network problem and provide estimations on quantum annealer's hardware requirements to enable a proper QUBO problem embedding of larger networks. We achieved the QUBO embedding of networks with up to 6 nodes on the D-Wave Advantage™. According to our estimates a real-sized network with 12 to 16 nodes require a QA hardware with at least 50000 qubits or more.
Resource allocation of wide-area internet networks is inherently a combinatorial optimization problem that if solved quickly, could provide near real-time adaptive control of internet-protocol traffic ensuring increased network efficacy and robustness, while minimizing energy requirements coming from power-hungry transceivers. In recent works we demonstrated how such a problem could be cast as a quadratic unconstrained binary optimization (QUBO) problem that can be embedded onto the D-Wave AdvantageTM quantum annealer system, demonstrating proof of principle. Our initial studies left open the possibility for improvement of D-Wave solutions via judicious choices of system run parameters. Here we report on our investigations for optimizing these system parameters, and how we incorporate machine learning (ML) techniques to further improve on the quality of solutions. In particular, we use the Hamming distance to investigate correlations between various system-run parameters and solution vectors. We then apply a decision tree neural network (NN) to learn these correlations, with the goal of using the neural network to provide further guesses to solution vectors. We successfully implement this NN in a simple integer linear programming (ILP) example, demonstrating how the NN can fully map out the solution space that was not captured by D-Wave. We find, however, for the 3-node network problem the NN is not able to enhance the quality of space of solutions.
Resource allocation of wide-area internet networks is inherently a combinatorial optimization problem that if solved quickly, could provide near real-time adaptive control of internet-protocol traffic ensuring increased network efficacy and robustness, while minimizing energy requirements coming from power-hungry transceivers. In recent works we demonstrated how such a problem could be cast as a quadratic unconstrained binary optimization (QUBO) problem that can be embedded onto the D-Wave Advantage™ quantum annealer system, demonstrating proof of principle. Our initial studies left open the possibility for improvement of D-Wave solutions via judicious choices of system run parameters. Here we report on our investigations for optimizing these system parameters, and how we incorporate machine learning (ML) techniques to further improve on the quality of solutions. In particular, we use the Hamming distance to investigate correlations between various system-run parameters and solution vectors. We then apply a decision tree neural network (NN) to learn these correlations, with the goal of using the neural network to provide further guesses to solution vectors. We successfully implement this NN in a simple integer linear programming (ILP) example, demonstrating how the NN can fully map out the solution space that was not captured by D-Wave. We find, however, for the 3-node network problem the NN is not able to enhance the quality of space of solutions.
In many complex networking systems, identifying critical nodes whose removal maximally disrupts network connectivity remains an important yet computationally challenging problem for network vulnerability analysis. Finding near-optimal solutions is known to be NP-hard. In this paper, we explore the potential of near-term quantum computing devices to efficiently solve the k-Critical Node Detection (k-CND) problem. We formulate the problem as a quadratic unconstrained binary optimization (QUBO), a mathematical optimization over binary variables amenable to solution on quantum annealers. We present a novel integer linear programming (ILP) formulation and its conversion into QUBOs and provide benchmarking results on D-Wave's quantum annealers. Our theoretical analysis proves that our proposed formulation, ILP2, generates substantially smaller QUBO than the state-of-the-art ILP1 formulation. Experimentally, our efficient QUBO yields a 59.7% decrease in QUBO variables on a graph with 10 vertices and 40 edges, and an 11.7% reduction in qubits on a 15-vertex, 22-edge graph compared to that of QUBO1. We analyze the solution quality and running time across quantum, classical, and hybrid solvers to assess the potential for quantum advantage. Our work showcases the promise and challenges of tackling this important graph problem on near-term quantum hardware.
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.
Embedding Service Function Chain (SFC) into the massive and resource-hungry Internet of Things (IoT) substrate graph is a critical optimization research problem. Unfortunately, the classical Integer Linear Programming (ILP) formulation for such problems is usually NP-hard. Thus, this research study presses on the need to go beyond the realms and employ Quantum Annealing (QA) to speed up the computation. To comply, we reformulate the SFC embedding problem into IoT graphs as Quadratic Unconstrained Binary Optimization (QUBO) format and propose a hybrid warm start quantum annealing (WSQA) optimization technique. Simulation results show that our proposed WSQA can improve resource utilization, accelerate computing time, and achieve a better scalability success rate at solving large-scale SFC deployment compared to standalone QA. Further along the line, this research inspires the application of quantum optimization for resource allocation in next-generation networks even with the limited availability of qubits.
This paper presents an application of a novel quadratic unconstrained binary optimization (QUBO) formulation to the minimum loss problem in distribution networks. The proposed QUBO formulation was conceived to be employed in quantum annealing-a quantum computing paradigm useful for solving combinatorial optimization problems. Quantum annealing is expected to provide better and/or faster solutions to optimization problems when compared to the ones provided by classical computers. With the problem at stake, better solutions result in lower energy losses, and faster solutions contribute to the same outcome given the future need for frequent reconfiguration of distribution networks to accommodate highly volatile demand, as anticipated by recent low-carbon solutions. The paper presents the results obtained through a hybrid quantum-classical solver for a standard 33-node test network and compares them with the ones obtained from classical solvers. Our main conclusion is that quantum annealing has potential to show advantage in the near future in terms of solution quality and time-to-solution, as quantum annealers and hybrid solvers continue to improve their performance.