Terra Quantum AG and Volkswagen Data Lab have collaborated to demonstrate the potential of hybrid quantum computing for the automotive industry. The capability to exponentially improve computing power with quantum enhanced properties promises a competitive advantage in different scenarios. Two research papers outline the joint effort and provide foundational scientific work for quantum-ready applications in automotive production: optimization of work-flow scheduling at the assembly line as well as hybrid machine learning to enhance image recognition systems for car classification.
Given the current state of quantum computing capabilities, the automotive industry is expected to benefit from the technology. Terra Quantum AG and Volkswagen Data: Lab have now collaborated to demonstrate real-world uses of the technology. The study team looked at two scenarios in which quantum technology could help the car industry: improving assembly line workflows and using quantum machine learning to improve image identification.
“The application of quantum technology will be a strategic step to ensure competitive advantage in the automotive industry. Quantum enabled data processing can deliver better results in shorter time windows. That means: better planning, time management and higher cost efficiency. Our applications can provide this business advantage for customers”
says Markus Pflitsch, Founder and CEO of Terra Quantum.
Terra Quantum can create application-specific hybrid quantum algorithms and deploy them on the QMware hybrid quantum cloud. The quantum and classical computing worlds are blended for increased performance in this fully integrated development environment. The united team shares its findings with the scientific community on arxiv after completing their applied research. The release of research publications demonstrating automobile industry applications highlights the possibility for industrial application.
The first study focuses on quality control testing in assembly-line procedures, which is a difficult optimization task. The united team is looking into ways to improve this final and crucial phase on the assembly process. Skilled employees undertake a series of tests and checks to assure product quality. The goal is to determine the most efficient and optimal test schedule. The complexity stems from the interdependence of testing and resource capability, such as shifting worker availability.
“Efficiently organizing specific time-sensitive tasks in a workflow is a major challenge. It includes a complex set of constraints and dependencies to consider. These variables present an intractable challenge for classic computers as the problem size increases. With our hybrid quantum computing approach, we can combine high-performance computing and leverage the unique capabilities of quantum mechanics. With this combination, we will be able to tackle the most complex computing challenges”
says Dr Florian Neukart, Chief Product Officer of Terra Quantum.
The collaborative team demonstrated the capability of the hybrid solution implemented on the QMware cloud by using a novel mathematical formulation and unique decomposition strategies for handling complexity. They provide a decomposition strategy for this specific application in the paper to reduce complexity and demonstrate the approach’s efficiency by achieving more optimal test schedules when compared to quantum, classical, and hybrid quantum-classical algorithms.
Machine learning is the focus of the second paper. The researchers wanted to increase image recognition accuracy. These systems are extremely useful in the industrial industry for fault detection, for example. The team created a new hybrid quantum machine learning algorithm, which was then deployed on the QMware cloud. They created a hybrid quantum residual neural network model with quantum elements and a new approach for a quantum-inspired tensor train hyperparameter optimization. As the problem size scaled, the researchers benchmarked this method against traditional machine learning approaches and saw performance advantages in the form of lower projected run durations and fitness. With fewer iterations, the newly proposed technique can improve image recognition accuracy.
Terra Quantum’s hybrid quantum computing approach is outlined in their latest White Paper and proves to outperform classical approaches selected. This superior computing capability opens the opportunity to create business advantage already today. The White Paper demonstrates the performance of their Hybrid Quantum Algorithm Libraries, as well as their execution capabilities on the hybrid quantum cloud QMware: combining high performance and quantum computing technologies in one integrated platform, Terra Quantum can deliver faster and more accurate results for complex computational problems.
In their latest White Paper, Terra Quantum explains how their hybrid quantum computing technique outperforms traditional ways. These enhanced computational capabilities allow businesses to gain a competitive advantage right now. The White Paper demonstrates the performance of their Hybrid Quantum Algorithm Libraries, as well as their execution capabilities on the hybrid quantum cloud QMware. Terra Quantum can deliver faster and more accurate results for complex computational problems by combining high-performance and quantum computing technologies in one integrated platform.