Quantum Computing: Towards Industry Reference Problems

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.

This is a pre-print of an article published in DIGITALE WELT Volume 5, issue 2. The final authenticated version is available online at: https://doi.org/10.1007/s42354-021-0335-7

References in corpus (7)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum algorithm for solving linear systems of equations
• Quantum computational advantage using photons
• A Quantum Approximate Optimization Algorithm
• Robust randomized benchmarking of quantum processes
• Benchmarking gate-based quantum computers
• How will quantum computers provide an industrially relevant computational advantage in quantum chemistry?