Quantum Computing: Towards Industry Reference Problems

TL;DR

This paper explores how quantum computing can address complex, high-value problems in the automotive industry, including simulation and optimization tasks, to overcome classical scalability limitations.

Contribution

It identifies key automotive problems suitable for quantum computing and discusses potential benefits of quantum-enhanced solutions across the industry.

Findings

Quantum computing can improve automotive simulation and optimization.

Several high-value problems in automotive industry are suitable for quantum solutions.

Quantum approaches may overcome classical scalability limitations.

Abstract

The complexity is increasing rapidly in many areas of the automotive industry. The design of an automobile involves many different engineering disciplines, e. g., mechanical, electrical, and software engineering. The software of a vehicle comprises millions of lines of code. Further, the manufacturing, logistics, distribution, and sales of a vehicle are highly complex. There is an immense need for solving simulation problems, e. g., in battery chemistry, an essential enabler for technological advancements for electric vehicles. In all these domains, myriads of optimization, simulation, and machine learning problems arise. Quantum computing-based approaches promise to overcome some of the inherent scalability limitations of classical approaches. This article investigates quantum computing applications across the automotive value chain and identifies several high-value problems that will benefit from quantum-enhanced solutions.