$$\mathrm{Q(AI)}^2$$: Quantum Artificial Intelligence for the Automotive Industry
Tobias Stollenwerk, Somtapa Bhattacharya, Michele Cattelan, Alessandro Ciani, G. Compostella, David Headley, Johannes Klepsch, Matthias Klusch, Markus Leder, Antonio Macaluso, Kristel F. L. Michielsen, Dmytro Nabok, Anestis Papanikolaou, Alexander Rausch, Marco Schumann, Andrea Skolik, Sheir Yarkoni, Frank K. Wilhelm
The goal of the project \(\mathrm{Q(AI)}^2\) was to acquire a broader basis of quantum computing enhanced AI and optimization algorithms for potential applications in the automotive industry. In particular, we strove to assess the potential for quantum advantage for specific use cases from the automotive industry. For this, we developed quantum algorithmic solutions tailored both to the available hardware as well as to the industrial problems at hand. In addition, we ensured industrial relevance of the investigated applications by the constitution of the consortium, involving all three large German car manufactures VW, Mercedes-Benz and BMW as well as Bosch as a central component supplier. Forschungszentrum Jülich and the German Center for Artificial Intelligence (DFKI) provided the necessary expertise in AI and quantum computing methods.
Q(AI)2: Quantum Artificial Intelligence for the Automotive Industry
The goal of the project (\mathrm{Q(AI)}^2) was to acquire a broader basis of quantum computing enhanced AI and optimization algorithms for potential applications in the automotive industry. In particular, we strove to assess the potential for quantum advantage for specific use cases from the automotive industry. For this, we developed quantum algorithmic solutions tailored both to the available hardware as well as to the industrial problems at hand. In addition, we ensured industrial relevance of the investigated applications by the constitution of the consortium, involving all three large German car manufactures VW, Mercedes-Benz and BMW as well as Bosch as a central component supplier. Forschungszentrum Jülich and the German Center for Artificial Intelligence (DFKI) provided the necessary expertise in AI and quantum computing methods.
Integrating quantum computing and artificial intelligence (AI) in automotive safety presents a paradigm shift, addressing complex challenges such as real-time traffic management, accident prevention, and system optimization. Quantum computing's principles of superposition, entanglement, and parallelism enhance AI's ability to process and analyze vast datasets, enabling precise and efficient decision-making in dynamic environments. Industry leaders like BMW, Volkswagen, and Waymo demonstrate the transformative potential of quantum-enhanced systems with applications in traffic optimization, autonomous navigation, and predictive maintenance. However, hardware scalability, ethical concerns, and regulatory gaps persist.
The field of quantum computing is presently undergoing a significant transition. Even if quantum computers are now so large and capable that they can only be used under certain limitations, be conventionally emulated, it is still unknown which applications can benefit from a quantum advantage. With an emphasis on artificial intelligence (AI) and its applications as some of the most significant key technologies of our time, this provided an excellent starting point for looking beyond the already demonstrated acceleration of a few specific classical computing tasks by quantum computers, such as unstructured search and prime factorization. Actually, the goal of Quantum Artificial Intelligence (QAI) is to merge the two worlds for each’s benefit. Stated differently, QAI research focuses on using quantum computing to solve computationally challenging AI problems and, conversely, using AI to address the difficulties associated with developing and running quantum computing systems. The creation of direct quantum or hybrid quantum-classical algorithms for problem solving and the examination of their possible benefits over the best classical alternatives are of particular interest.
The integration of quantum computing and artificial intelligence (AI) constitutes a bidirectional synergy that is reshaping both disciplines. This review investigates the reciprocal relationship in which AI addresses foundational challenges in quantum computing, while quantum computing offers the potential to advance machine learning beyond classical constraints. Recent advancements exemplify this interaction: Google DeepMind’s AlphaQubit neural network decoder has achieved state-of-the-art quantum error correction, improving performance by 6% over tensor networks and 30% over correlated matching methods. Additionally, quantum neural networks have demonstrated exponential improvements in sample complexity for specific learning tasks. This review systematically examines the current landscape of quantum-AI integration across three primary dimensions. First, it addresses AI-enhanced quantum systems, such as transformer-based error correction, reinforcement learning for circuit optimization, and AI-driven hardware calibration. Second, it explores quantum-accelerated machine learning algorithms, including variational quantum neural networks, quantum generative adversarial networks, and quantum reinforcement learning. Third, it evaluates industry deployments in sectors such as life sciences, financial services, climate modeling, and pharmaceutical development. Notable examples include IonQ’s quantum chemistry simulations, which achieved 40% efficiency improvements in carbon capture material design; St. Jude’s identification of KRAS protein inhibitors using quantum machine learning with experimental validation; and JP Morgan Chase’s implementation of quantum portfolio optimization. The analysis indicates that the field has advanced from preliminary demonstrations to production-grade applications, especially in drug discovery and molecular simulation, where quantum computing offers measurable benefits. Nevertheless, several challenges remain, including barren plateaus in variational algorithms, scalability constraints in current noisy intermediate-scale quantum (NISQ) devices, requirements for real-time decoding speed, and the necessity for fault-tolerant quantum computing (FTQC) systems. IBM’s 2029 roadmap, which targets 200 logical qubits capable of executing 100 million gates, together with progress in high-rate quantum low-density parity-check codes, outlines a trajectory toward practical fault tolerance. This review makes two principal research contributions: Comprehensive Evaluation Framework for Quantum Advantage Assessment: We establish the first systematic, integrated methodology for determining quantum computing feasibility that combines problem characterization, resource estimation, quantum advantage assessment, and quantum algorithm paradigm selection. This framework consolidates criteria scattered across academic literature and industry practice into a unified decision-making tool applicable across chemistry, optimization, machine learning, and simulation domains. The framework addresses a critical industry need: enabling non-expert practitioners (chemists, financial analysts, materials scientists) to objectively assess quantum computing suitability without requiring deep quantum expertise. Novel Quantum Resource Optimization Algorithms: We present three concrete algorithmic contributions advancing quantum-AI integration: • Data Encoding Efficiency Algorithm that automatically selects optimal qubit encoding strategies (amplitude vs. angle encoding) minimizing total quantum resource consumption • Error Budget Optimization Algorithm that iteratively determines optimal quantum error correction code distance balancing logical error rate targets against physical qubit overhead • Real-time Hardware Specification Aggregation from multiple quantum cloud platforms (IBM Quantum, Amazon Braket, Google, IonQ) enabling dynamic feasibility assessment as hardware capabilities evolve Combined, these contributions establish quantum computing advantage assessment as a rigorous, data-driven discipline rather than ad-hoc expert judgment. The framework enables strategic quantum computing investment decisions, accelerates problem identification for early quantum utility, and provides a reference methodology for standardizing quantum advantage evaluation across academia and industry. This review synthesizes insights from recent academic literature, industry implementations, and expert perspectives to deliver a comprehensive assessment of quantum-AI synergies grounded in this evaluation framework. The mathematical foundations, ranging from quantum Fourier transforms to Gaussian processes, are examined. Best practices for researchers and practitioners are also outlined. The findings suggest that, although universal quantum advantage has not yet been realized, domain-specific quantum-AI applications in chemistry, optimization, and sensing are achieving practical utility in 2024-2025. This development marks a significant transition from theoretical potential to commercial realization, informed by rigorous evaluation methodologies.
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.
Quantum - Enhanced Artificial Intelligence QAI merges the advanced capabilities of quantum computing with the sophisticated algorithms of artificial intelligence. This paper explores the current challenges in developing quantum algorithms and qubits, addressing issues of fidelity and scalability. It also examines the potential applications of QAI in various industries, including healthcare, cryptography, and optimization. This article also highlights the transformative potential of QAI, showcasing how the integration of quantum computing with AI can overcome current limitations in data processing and optimization, leading to groundbreaking advancements across multiple sectors.
Quantum Artificial Intelligence (QAI) combines quantum computing with artificial intelligence (AI) to surpass the traditional barriers of classical computing systems, such as their inability to solve large-scale optimization problems or simulate complex quantum systems, which require exponentially increasing computational resources. Quantum computing, on the other hand, makes use of quantum mechanical phenomena of superposition, entanglement, and interference to process millions of data points in parallel, avoiding the exponential computation needed in many complex problems. QAI combines principles of quantum computing with AI techniques, thereby accelerating model learning. Some of the most notable applications include drug discovery in terms of exact predictions of molecular interactions, due to the enhancement of quantum simulations. There are cryptographic applications where QAI challenges classical encryption but also contains more advanced, non-breakable quantum cryptographic methods. In the same way as general advances in the field of data analysis and optimization, these domains would change: financial modeling, cybersecurity, and climate science. The biggest challenges remaining in scaling quantum hardware are noise and decoherence. Solutions will require major advances in quantum error correction, along with hybrid quantum-classical systems integrating the strengths of each. At this point, added to the list of critical concerns in the development of QAI are ethical considerations such as data privacy, algorithmic bias, and transparency. But all this notwithstanding, QAI has the transformative potential to solve problems that classical AI was unable to solve and drive innovation in domains ranging from healthcare and environmental science to cryptography and beyond. Unlocking its full capabilities and overcoming the resultant technical and ethical challenges is by way of further research and collaboration.