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IEEE International Conference on Quantum Computing and Engineering (QCE)

Optimization of Base Station Power Supply Selection by Quantum Annealing

Chihiro Dogo, Kazuhiro Saito, Nozomu Togawa

Published Sep 15, 2024• 4 citations
Publisher

Abstract

In this poster, we use quantum annealing to solve the optimal operation for a photovoltaic-powered 5G base station, and discuss its usefulness and quality. The formulated QUBO for quantum annealing works on D-Wave quantum machine, and the results showed that quantum annealing can produce 100 times faster and better quality solutions in comparison with Simulated Annealing(SA). Scaling experiments showed that quantum annealing is more likely to produce good solutions with decision variables of around 50~100, and splitting the problem gave better results than SA in various scaling.

DOI
10.1109/qce60285.2024.10329

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In this poster, we use quantum annealing to solve the optimal operation for a photovoltaic-powered 5G base station, and discuss its usefulness and quality. The formulated QUBO for quantum annealing works on D-Wave quantum machine, and the results showed that quantum annealing can produce 100 times faster and better quality solutions in comparison with Simulated Annealing(SA). Scaling experiments showed that quantum annealing is more likely to produce good solutions with decision variables of around 50~100, and splitting the problem gave better results than SA in various scaling.
As global energy demand continues to rise, efficient energy management is essential to support the growing integration of renewable energy in the energy mix while controlling operational costs. This paper introduces a method that leverages non-exact Quadratic Unconstrained Binary Optimization (QUBO) formulation combined with tailored post-processing to boost the optimization of the energy management problem of 5G base stations. Unlike traditional rule-based approaches, which rely heavily on domain expertise and manual design, we propose a systematic QUBO-based framework suitable for Quantum Annealing (QA). The proposed formulation significantly reduces the number of decision variables (from 1995 in an exact QUBO to approximately 144) making it compatible with current QA hardware while producing high-quality solutions. Furthermore, the post-processing steps further improve solution quality, increasing the global optimal solution achievement rate by 10–15% depending on the solver. Experimental results demonstrate that the proposed method is deployable on current hardware and achieves performance comparable to conventional rule-based systems. Such observations highlight the potential of such designs in solving real-world optimization challenges.
In current mobile networks, optimizing which base station a mobile phone in a particular area connects to is crucial for ensuring good communication quality for each mobile phone but presents a challenging combinatorial optimization problem. In this study, we optimize the connection patterns to base stations using quantum annealing which is a heuristic optimization algorithm using quantum fluctuations. However, since the number of qubits on a quantum annealer is limited, it is necessary to consider a formulation that efficiently utilizes qubits. By adopting a variable reduction formulation, we significantly reduce the qubit requirements compared to the naive formulation that is typically used when considering pattern-matching problems. Furthermore, experiments using quantum annealing revealed that the accuracy of the approximate solution obtained by the new formulation is superior to that of the conventional formulation. In addition, we demonstrate that the new formulation provides better solutions than the conventional formulation as the problem size increases, even when using simulated annealing, the classical counterpart of quantum annealing.
In planning and scheduling, large-scale optimization problems often have complex limits and a lot of factors, which makes them hard to answer with standard methods. Quantum annealing has become a hopeful way to deal with these problems because it can quickly look into a lot of possible solutions. This essay looks into how quantum annealing might be used to solve optimization problems in transportation and scheduling, with a focus on how it could be used for route planning, inventory management, and arranging workers. To do these optimization jobs, quantum annealing is used. This is a quantum computing method that gets the global minimum of a cost function. We use the D-Wave quantum annealer in this method. It turns the problem's limits and objective function into a quadratic unconstrained binary optimization (QUBO) model. The quantum annealer then works on this model to find the best answers. The suggested method combines traditional preparation methods with quantum annealing to get the best results for big schedule and transportation issues. We use quantum annealing to solve difficult multi-objective optimization problems, like reducing shipping time, cost, and resource use all at the same time. Preliminary results show that quantum annealing can make solutions much better and use computers much more efficiently than traditional methods for optimizing supplies and schedules. Quantum annealing works especially well with problems that have a lot of dimensions. It can provide scalable answers that sometimes work better than standard methods.
Abstract The quantum annealing machine manufactured by D-Wave Systems is expected to find the optimal solution for QUBO (Quadratic Unconstrained Binary Optimization) accurately and quickly.This would be useful in future applications where real-time calculation is needed.One such application is traffic signal optimization.Some studies use quantum annealing for this.However, they are formulated in unrealistic settings, such as only crossroads on the map.Therefore, we suggest a QUBO, which can deal with T-junctions and multi-forked roads.To validate the efficiency of our approach, SUMO (Simulation of Urban MObility) is used.This enables us to experiment with geographic information data very close to the real world.We compared results with those using the Gurobi Optimizer in the experiment to confirm that quantum annealing can find a ground state.The results show that the quantum annealing cannot find the ground state, but our model can reduce the time that vehicles wait at a red light.It is also inferior to the Gurobi Optimizer in calculation time. This seems to be due to the D-Wave machine's hardware limitations and noise effects, such as ambient temperature.If these problems are solved, and the number of qubits is increased, the use of quantum annealing is likely to be superior in terms of the speed of calculating an optimal solution.
Quantum annealing (QA) has shown good solutions quickly in benchmarks of combinatorial optimization problems. There are not many real-world problems that illustrate the effectiveness of QA compared to the typical optimization methods. We applied QA to a shift scheduling problem for call centers with four conditions. The shift scheduling problem for call centers optimizes shifts for a specific period of time while satisfying various constraints, such as satisfying the desired worker's shifts. In this paper, we formulated the shift scheduling problem for call centers in QUBO to apply QA. We tuned various parameters required to execute the QA. Then, we compared QA with six typical optimization methods using a classical computer to investigate QA's effectiveness for the shift scheduling problem for call centers. The results show that for three instances with up to 126 variables, QA is about 3-14 times faster than other methods and obtains enough quality of solutions. This shows that QA can be one of the effective methods to solve practical applications in the real-world.
Quantum annealing is getting increasing attention in combinatorial optimization. The quantum processing unit by D-Wave is constructed to approximately solve Ising models on so-called Chimera graphs. Ising models are equivalent to quadratic unconstrained binary optimization (QUBO) problems and maximum cut problems on the associated graphs. We have tailored branch-and-cut as well as semidefinite programming algorithms for solving Ising models for Chimera graphs to provable optimality and use the strength of these approaches for comparing our solution values to those obtained on the current quantum annealing machine, D-Wave 2000Q. This allows for the assessment of the quality of solutions produced by the D-Wave hardware. In addition, we also evaluate the performance of a heuristic by Selby. It has been a matter of discussion in the literature how well the D-Wave hardware performs at its native task, and our experiments shed some more light on this issue. In particular, we examine how reliably the D-Wave computer can deliver true optimum solutions and present some surprising results.