A Hybrid Quantum-Classical Approach Using D-Wave and SLSQP for Double Wishbone Suspension System Optimization

The optimization of double wishbone suspension system is highly important for high performance vehicles, as such vehicles have specific needs in term of wheel control and also subject to their mechanical constraints. This paper proposes a new optimization approach, building on the previous work that...

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Bibliographic Details
Published in2025 IEEE Conference on Artificial Intelligence (CAI) pp. 16 - 19
Main Authors Arshad, Muhammad Waqas, Lodi, Stefano, Liu, David Q.
Format Conference Proceeding
LanguageEnglish
Published IEEE 05.05.2025
Subjects
Accuracy
Annealing
D-Wave
Double Wishbone Suspension
Engineering Applications
Hybrid Optimization
Optimization
Programming
Quantum annealing
Sequential Least Squares Programming (SLSQP)
Stability criteria
Suspensions (mechanical systems)
Three-dimensional displays
Visualization
Wheels
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Summary:The optimization of double wishbone suspension system is highly important for high performance vehicles, as such vehicles have specific needs in term of wheel control and also subject to their mechanical constraints. This paper proposes a new optimization approach, building on the previous work that evaluated several optimization algorithms to minimize camber angle variations. A quantum computing platform called D-Wave is used for quantum annealing, and a classical model called Sequential Least Squares Programming (SLSQP) is used as a hybrid framework. This integration makes use of both quantum annealing search and SLSQP local refinement to improve the accuracy of the optimization process, which makes the optimization work better. The hybrid approach is seen to outperform the quantum annealing approach in terms of accuracy, efficiency of computation, and constraint satisfaction. These findings show that there is a great possibility of applying the hybrid quantum-classical approach in the iterative design of suspension systems and other such engineering structures.
DOI:10.1109/CAI64502.2025.00009