Andreas Bayerstadler, Guillaume Becquin, Julia Binder, Thierry Botter, Hans Ehm, Thomas Ehmer, Marvin Erdmann, Norbert Gaus, Philipp H. P. Harbach, Maximilian Hess, Johannes Klepsch, Martin Leib, Sebastian Markus Luber, André Luckow, Maximilian Balthasar Mansky, Wolfgang Mauerer, Florian Neukart, Christoph A. Niedermeier, Lilly Palackal, Ruben Pfeiffer, Carsten Polenz, Johanna Sepúlveda, Tammo Sievers, Brian Standen, Michael Streif, Thomas Strohm, Clemens Utschig-Utschig, Daniel Volz, Horst Weiß, Fabian Winter
Abstract Quantum computing promises to overcome computational limitations with better and faster solutions for optimization, simulation, and machine learning problems. Europe and Germany are in the process of successfully establishing research and funding programs with the objective to advance the technology’s ecosystem and industrialization, thereby ensuring digital sovereignty, security, and competitiveness. Such an ecosystem comprises hardware/software solution providers, system integrators, and users from research institutions, start-ups, and industry. The vision of the Quantum Technology and Application Consortium (QUTAC) is to establish and advance the quantum computing ecosystem, supporting the ambitious goals of the German government and various research programs. QUTAC is comprised of ten members representing different industries, in particular automotive manufacturing, chemical and pharmaceutical production, insurance, and technology. In this paper, we survey the current state of quantum computing in these sectors as well as the aerospace industry and identify the contributions of QUTAC to the ecosystem. We propose an application-centric approach for the industrialization of the technology based on proven business impact. This paper identifies 24 different use cases. By formalizing high-value use cases into well-described reference problems and benchmarks, we will guide technological progress and eventually commercialization. Our results will be beneficial to all ecosystem participants, including suppliers, system integrators, software developers, users, policymakers, funding program managers, and investors.
Abstract Quantum computing promises to overcome computational limitations with better and faster solutions for optimization, simulation, and machine learning problems. Europe and Germany are in the process of successfully establishing research and funding programs with the objective to advance the technology’s ecosystem and industrialization, thereby ensuring digital sovereignty, security, and competitiveness. Such an ecosystem comprises hardware/software solution providers, system integrators, and users from research institutions, start-ups, and industry. The vision of the Quantum Technology and Application Consortium (QUTAC) is to establish and advance the quantum computing ecosystem, supporting the ambitious goals of the German government and various research programs. QUTAC is comprised of ten members representing different industries, in particular automotive manufacturing, chemical and pharmaceutical production, insurance, and technology. In this paper, we survey the current state of quantum computing in these sectors as well as the aerospace industry and identify the contributions of QUTAC to the ecosystem. We propose an application-centric approach for the industrialization of the technology based on proven business impact. This paper identifies 24 different use cases. By formalizing high-value use cases into well-described reference problems and benchmarks, we will guide technological progress and eventually commercialization. Our results will be beneficial to all ecosystem participants, including suppliers, system integrators, software developers, users, policymakers, funding program managers, and investors.
This article examines the sectoral prospects for the implementation of quantum technologies within the paradigm of Industry 4.0 and the digital transformation of industrial systems. The study addresses the growing problem of technological asymmetry and the limitations of classical computational methods in managing complex industrial processes characterized by high uncertainty, large data volumes, and nonlinear dynamics. The research aims to identify the key sectors of Industry 4.0 where quantum technologies—particularly quantum computing, quantum communication, and quantum sensing—can provide significant competitive advantages. The article analyzes the potential application of quantum algorithms for optimization, risk modeling, predictive analytics, logistics, cybersecurity, and advanced materials engineering. It also evaluates institutional and infrastructural constraints affecting quantum technology adoption, including the lack of skilled human capital, high implementation costs, and regulatory uncertainty. The findings demonstrate that the integration of quantum technologies into industrial ecosystems will be uneven across sectors, with the highest potential in finance, energy, logistics, pharmaceuticals, and advanced manufacturing. The study concludes that the successful deployment of quantum solutions requires a hybrid technological architecture combining classical and quantum systems, as well as coordinated policies in innovation governance, investment, and education.
Quantum computing as emerging technology raises high expectations to accelerate complicated calculations, superseding capabilities of presently available supercomputers and to provide a solution to the ever-increasing challenge of processing big amount of data. Besides theoretical promises, quantum computers have successfully been realized in experimental settings during the past few years, and can now also be accessed commercially. Given this availability of quantum computers, their application potential can be scrutinized. Promised to lead to disruptive changes in addressing and solving problems from the domains of simulation, optimization and machine learning, quantum computers have been researched in context of many application scenarios in industrial and academic setting in the recent years. However, it turns out that currently available quantum computers are still to small in size and limited in quality to fulfill the high hopes set in them at the moment. This review reflects on the current state of the technology coming from the perspective of the application, and indicates the developments required on hardware, software and algorithmic side to eventually make quantum computing beneficial for applications.
Computational technologies drive progress in industry, science, government, and society. While these technologies form the foundation for intelligent systems and enable scientific and business innovation, they are also the limiting factors for progress. Quantum computing promises to overcome these limitations with better and faster solutions for optimization, simulation, and machine learning problems. Quantum computing is broadly applicable to business problems in optimization, machine learning, and simulation, impacting all industries. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Therefore, it is instrumental for industry to seek an active role in this emergent ecosystem. In this chapter, the authors present a brief overview on various applications of quantum computing in machine learning.
Quantum computing is increasingly seen as a disruptive technology capable of expanding the computational limits of advanced manufacturing systems within the emerging Industry 5.0 framework. By utilizing quantum mechanical principles such as superposition, entanglement, and quantum parallelism, quantum computation enables new approaches to solving complex optimization, simulation, and data-intensive problems that are challenging or impractical for classical computers. This paper offers a comprehensive and critical review of the potential impacts of quantum computing on advanced manufacturing, focusing on intelligent production planning, supply chain optimization, materials discovery, predictive maintenance, and human–machine collaboration, key aspects of Industry 5.0. The originality of this review lies in its integrated analysis of quantum computing alongside artificial intelligence, digital twins, and cyber–physical systems, highlighting how these technologies, when combined, improve decision-making speed, process efficiency, and sustainability. Despite these opportunities, the integration of quantum computing into Industry 5.0 systems faces critical challenges, including hardware limitations, algorithm scalability, data security concerns, workforce readiness, and the complexity of integrating quantum solutions with existing industrial infrastructures. The role of hybrid quantum-classical architectures is examined as a feasible and transitional approach for near-term manufacturing applications. By critically assessing both technological strengths and practical constraints, this review positions quantum computing as a promising enabler of resilient, human-centered, and sustainable manufacturing ecosystems. The insights aim to assist researchers, industry players, and policymakers in strategically managing the integration of quantum technologies as manufacturing systems advance toward Industry 5.0.
Despite the significant scientific and engineering hurdles in advancing quantum computers, notable strides are being made toward utilizing this technology in commercial domains. Quantum computers are projected to exceed the computational power of classical computers within the next decade, potentially revolutionizing various industries. This study explores a range of fields that have already begun integrating quantum hardware. By presenting these as examples of combinatorial problems, we demonstrate their applications across three key sectors: cryptography, material science, and Machine Learning. Further, we will conduct a survey on various industries and companies to identify the broader usage of quantum computing in their manufacturing. The survey will analyze the diverse potential applications and current advantages of quantum hardware and algorithms. The results will help further researchers identify and segment the companies and understand the rapid application of quantum computing.
Quantum computing serves as a revolutionary paradigm in computing, harnessing quantum mechanics principles such as quantum entanglement and superposition for optimization simulation, machine learning, and other problems that are a challenge to classical computer systems. The article will examine different usage opportunities in retail and supply chain operations, healthcare and life sciences domains, insurance operations, and financial service industries. Better computation can be achieved with quantum algorithms in discrete and continuous optimization, logistics network optimization, warehouse management, and product delivery optimized by quantum algorithms. Application domains in healthcare include simulation of molecules for medical breakthroughs, genomic analysis, and medical studies optimized by quantum eigenvalue algorithms and quantum eigensolver algorithms. The insurance industry portfolio targets catastrophic modeling, identification, and risk assessment optimized by quantum amplitude estimation algorithms and quantum machine learning algorithms. Financial service industries include derivative pricing, portfolio optimization, credit risk evaluation, and real-time risk calculation optimized in quantum algorithms. The path towards achieving quantum benefit in quantum technology involves using noisy intermediate quantum technology with hybrid quantum and classical designs and error management schemes, where integration opportunities for different industries arise from their common computation problem schemas in need of further scaling in hardware technology.