The growing complexity of products
and manufacturing processes has intensified calls for a significant
increase in computing power in recent years. In fields such as materials
science, pharmaceutical research and artificial intelligence, even
high-performance computing systems are increasingly reaching their limits. The demand for more powerful machines capable of handling
extremely complex and computation-intensive tasks, including AI-driven
applications, continues to rise.
Quantum computing – key principles and capabilities
- Quantum computers process information using qubits, which can exist in multiple states simultaneously through superposition and entanglement, enabling fundamentally new computing approaches.
- Unlike classical computers, quantum systems can evaluate many possible solutions in parallel, offering advantages for complex optimisation, simulation and search problems.
- Due to their sensitivity to environmental disturbances, quantum computers require extreme operating conditions and are currently used as specialised systems rather than general-purpose computers.
What began as a theoretical concept in the 1960s and 1970s,
developed by IBM researchers such as Richard Feynman and Yuri Manin, has since
evolved into one of the most promising research fields in computer science:
quantum computing. This technology has the potential to play a key role in
solving problems that remain intractable for classical computers, including
today’s most powerful supercomputers.
“Those who master and apply quantum technologies will gain
decisive competitive advantages,” said Achim Berg, then President of Bitkom,
several years ago. According to a Bitkom Research study based on interviews
with 605 companies employing more than 20 people, around 54 per cent believe
quantum computing will be highly relevant to the future competitiveness of the
German economy. The perceived importance increases with company size, rising to
74 per cent among enterprises with more than 2,000 employees.
Strategic interest grows within the automotive industry
The automotive sector is also showing growing strategic
interest in quantum computing. One example is BMW Group, which has been
systematically building internal expertise in the field since 2017. Following
several years of evaluation, the company defined its own quantum strategy in
2020.
“We view quantum computing as a strategic future technology
that can support our core business and position us as an innovation leader,”
explains Andre Luckow, Head of Innovation and Emerging Technologies at BMW
Group. “At the same time, our activities aim to strengthen Europe’s digital
sovereignty.”
For BMW, the focus is less on short-term breakthroughs and
more on long-term capability building – including knowledge, software expertise
and the ability to assess potential applications realistically.
What is a quantum computer?
The operating principle of a quantum computer differs
fundamentally from that of classical systems. While conventional computers
process information using bits that represent either zero or one, quantum
computers use so-called qubits. These exploit quantum-mechanical effects, most
notably superposition, which allows a qubit to exist in multiple states
simultaneously until it is measured.
This enables quantum computers to consider many possible
computational states in parallel rather than sequentially. For certain classes
of problems – such as optimisation, complex searches or simulations – this
parallelism can offer substantial efficiency gains. Another key phenomenon is
entanglement, in which the states of multiple qubits are intrinsically linked.
This allows computations that cannot be replicated using classical
architectures.
Early experiments with quantum computers date back to the
1980s, when only a small number of highly unstable qubits could be controlled.
Advances in physics, materials science and system control have since led to
significant improvements in stability and controllability. However, qubits
remain extremely sensitive to environmental disturbances such as temperature
fluctuations or electromagnetic interference. As a result, many systems require
extreme cooling close to absolute zero, vacuum operation or sophisticated
shielding.
This technical complexity underlines that quantum computers
are not intended to replace classical systems but rather to complement them as
specialised tools for selected problem domains.
Understanding the quantum computing stack
To realistically assess the industrial potential of quantum
computing, it must be viewed as a layered system. At the lowest level lies the
hardware layer, which focuses on the physical realisation of quantum
processors. For automotive OEMs and suppliers, this layer is primarily relevant
through research collaborations rather than in-house development.
A critical intermediate layer is quantum error correction,
which will ultimately determine whether quantum computers can deliver reliable,
industrial-grade results. Progress here will define when quantum computing
transitions from experimental pilots to robust applications.
Above this sit logical qubits and abstraction layers that
make quantum systems usable within existing IT and simulation environments.
Quantum intermediate representations allow algorithms to be formulated
independently of specific hardware platforms, protecting investments in
software and expertise.
At the application layer, quantum computing meets real-world
automotive challenges, including optimisation problems, complex simulations and
hybrid approaches combining quantum computing with artificial intelligence.
Importantly, meaningful progress at the application level is already possible
even while hardware maturity remains limited.
From qubit growth to system architecture
Parallel to standardisation efforts, quantum hardware has
advanced rapidly. IBM, for example, has consistently increased qubit counts,
progressing from the 65-qubit Hummingbird processor to later generations such
as Eagle, Osprey and Condor. At the same time, the focus has shifted from pure
scaling towards system architecture, control and software integration.
Key Facts: Why quantum computing matters for the automotive industry
- Automotive manufacturers are exploring quantum computing to address highly complex optimisation and simulation tasks in vehicle development, production, logistics and materials research.
- OEMs such as BMW focus on long-term capability building, including software expertise and quantum-inspired algorithms that can already be applied on classical hardware.
- Hybrid approaches combining quantum computing and artificial intelligence are being investigated to improve efficiency in data-intensive applications, while broad industrial deployment is still considered a long-term prospect.
A key milestone for European quantum research was the
installation of IBM Quantum System One in Ehningen, Germany, in 2021. Operated
in collaboration with Fraunhofer, the system provides companies and research
institutions with practical access to quantum computing under German legal
frameworks, offering added security in terms of data protection and
intellectual property.
Quantum computing as an industrial ecosystem
The founding of the Quantum Technology and Application
Consortium (QUTAC) in 2021 provided further momentum. Members include Bosch,
BMW, Volkswagen, BASF, Covestro and SAP. The consortium aims to accelerate the
transfer of quantum research into industrially relevant applications across
sectors such as automotive, chemistry, pharmaceuticals and insurance.
In parallel, initiatives such as Munich Quantum Valley are
strengthening Germany’s quantum ecosystem through close collaboration between
industry, academia and public institutions, supported by extensive public
funding.
Where quantum computing could matter for OEMs
BMW’s quantum strategy illustrates how OEMs are approaching
the technology pragmatically. The company is focusing on optimisation problems
across production and logistics, including vehicle sequencing, resource
planning and robot trajectory optimisation. In vehicle development,
applications include the design of electrical and thermal systems as well as
complex vehicle architectures.
Quantum-inspired algorithms, which can already run on
classical hardware, play a key role at this stage, allowing BMW to build
expertise without committing to specific hardware platforms.
Hybrid quantum–AI approaches gain attention
Beyond individual OEM initiatives, hybrid approaches
combining quantum computing and artificial intelligence are gaining traction.
One example is the QAIAC project led by Forschungszentrum Jülich in
collaboration with partners such as Mercedes-Benz, ZF and the German Research
Center for Artificial Intelligence (DFKI).
The project explores how quantum computing could enhance AI
applications in areas such as transport route planning, production scheduling
and finite element analysis. Hybrid methods aim to combine quantum-supported
algorithms with classical AI to address highly complex, data-intensive tasks
more efficiently.
Researchers emphasise that these applications are still at
an early stage. However, they underline the long-term potential of quantum
computing as a strategic technology that could reshape automotive development,
production and mobility systems over the coming decades.
