Multi-car paint shop optimization with quantum annealing

Yarkoni, Sheir; Alekseyenko, Alex; Streif, Michael; Dollen, David Von; Neukart, Florian; Bäck, Thomas

doi:10.1109/qce52317.2021.00019

Cited by 38 publications

(27 citation statements)

References 28 publications

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“…By using Eq. 2, the Ising model can be converted into QUBO and vice versa, so we can choose the formulation that is easier to formulate depending on the problem．In this paper, we formulate the shift scheduling problem for call centers in QUBO． While there has been prior work that applies QA to the real problems (traffic flow modeling [19], air traffic management [21], multi-car paint shop [23], and nurse scheduling [8,16]), ours is the first work that clarifies the effectiveness of the QA with a real quantum machine for the shift scheduling problem for call centers with comparison to classical methods to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Applying Quantum Annealing for Shift Scheduling Problem for Call Centers

Hamada

Saito

Kawashima

2023

IJNC

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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.

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Section: Introductionmentioning

confidence: 99%

Applying Quantum Annealing for Shift Scheduling Problem for Call Centers

Hamada

Saito

Kawashima

2023

IJNC

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“…Motivated by this, quantum computing has not only received much attention in the research community, but was also started to be perceived as a valuable technology in industry. Volkswagen published a pioneering result on using the D-Wave quantum annealer to optimize traffic flow in 2017 [2], which prompted a number of works by other automotive companies [3][4][5]. Since then, quantum annealing has been applied in a number of industry-related problems like chemistry [6,7], aviation [8], logistics [9] and finance [10].…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter optimization of hybrid quantum neural networks for car classification

Sagingalieva¹,

Kurkin²,

Melnikov³

et al. 2022

Preprint

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Image recognition is one of the primary applications of machine learning algorithms. Nevertheless, machine learning models used in modern image recognition systems consist of millions of parameters that usually require significant computational time to be adjusted. Moreover, adjustment of model hyperparameters leads to additional overhead. Because of this, new developments in machine learning models and hyperparameter optimization techniques are required. This paper presents a quantum-inspired hyperparameter optimization technique and a hybrid quantum-classical machine learning model for supervised learning. We benchmark our hyperparameter optimization method over standard black-box objective functions and observe performance improvements in the form of reduced expected run times and fitness in response to the growth in the size of the search space. We test our approaches in a car image classification task, and demonstrate a full-scale implementation of the hybrid quantum neural network model with the tensor train hyperparameter optimization. Our tests show a qualitative and quantitative advantage over the corresponding standard classical tabular grid search approach used with a deep neural network ResNet34. A classification accuracy of 0.97 was obtained by the hybrid model after 18 iterations, whereas the classical model achieved an accuracy of 0.92 after 75 iterations.

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“…Quantum annealers are commercially available by the D-Wave company [23], and a vast amount of research has been devoted to identifying potential use cases [1]. D-Wave quantum annealers have been utilized to solve problems from different domains such as transportation [11,12,33,42], finance [26,32], chemistry [17,29,37], and computer science [4,22].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Production of Test Vehicles using Hybrid Constrained Quantum Annealing

Glos¹,

Kundu²,

Salehi³

2022

Preprint

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Optimization of pre-production vehicle configurations is one of the challenges in the automotive industry. Given a list of tests requiring cars with certain features, it is desirable to find the minimum number of cars that cover the tests and obey the configuration rules. In this paper, we model the problem in the framework of satisfiability and solve it by utilizing the newly introduced hybrid constrained quadratic model (CQM) solver provided by D-Wave. The problem definition is based on the "Optimizing the Production of Test Vehicles" use case given in the BMW Quantum Computing Challenge. We formulate a constrained quadratic model for the problem and use a greedy algorithm to configure the cars. We benchmark the results obtained from the CQM solver with the results from the classical solvers like CBC (Coin-or branch and cut) and Gurobi. We conclude that the performance of the CQM solver is comparable to classical solvers in optimizing the number of test vehicles. As an extension to the problem, we describe how the scheduling of the tests can be incorporated into the model.

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Multi-car paint shop optimization with quantum annealing

Cited by 38 publications

References 28 publications

Applying Quantum Annealing for Shift Scheduling Problem for Call Centers

Applying Quantum Annealing for Shift Scheduling Problem for Call Centers

Hyperparameter optimization of hybrid quantum neural networks for car classification

Optimizing the Production of Test Vehicles using Hybrid Constrained Quantum Annealing

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