SAP: Successful interaction – quantum computing and generative AI

The challenge: AI requires ever-increasing computing power

AI models such as large language models (LLMs) have made significant progress in automation, natural language understanding and creative problem solving. However, massive computing power is required to train and deploy these models. Traditional hardware architectures based on classical computer systems are struggling to keep up with these requirements.

This has led to slower innovation cycles, rising costs and scalability limitations. Solving optimization problems in areas such as logistics, finance and supply chains remains costly and time-consuming, even when using cutting-edge AI solutions.

Other barriers that cannot be fully overcome with current AI infrastructure include data privacy issues, lack of energy inefficiency and processing bottlenecks. This is where quantum computing comes into play.

In order to harness the power of quantum computing for generative AI, companies need to adapt their AI strategies. It is not enough to simply transfer the AI models currently in use to quantum computers. This transformation requires the following changes:

Developing AI models specifically for quantum computing

Current AI models are based on the principles of conventional computer systems. They are primarily based on classic machine learning and deep learning algorithms. However, quantum computing works according to completely different principles such as superposition, entanglement and quantum interference, which can support much more powerful AI models. Companies are required to put their AI model architecture to the test. They must supplement this architecture with quantum computing principles and algorithms in order to open up new possibilities for decision-making processes, pattern recognition and optimization.

This offers executives a strategic opportunity: pioneers in the development of AI specifically for quantum computing will secure a lead over their competitors. The corresponding models are able to solve problems that cannot currently be solved by conventional systems – and do so much more precisely and quickly. Examples of such problems include the dynamic real-time optimization of supply chains and complex scenarios with multiple variables.

Hybrid AI approaches

It will be a while before there are market-ready AI models specifically for quantum computing. Initially, hybrid systems that combine classic AI with the possibilities of quantum computing will therefore probably be used. With such a hybrid model, companies can tackle certain tasks, such as optimization or data processing, where quantum computing can achieve particularly good results. Tasks such as complex routing or forecasting, for example, can be supplemented with algorithms based on quantum computing, while traditional AI continues to be used in other areas of the company.

With a hybrid approach, managers can combine current AI technologies with the future possibilities of quantum computing. This allows companies to remain competitive without having to switch to completely new technologies. By using hybrid solutions, companies can benefit from the advantages of both technologies while gradually transitioning to quantum-optimized AI. Successful examples of hybrid systems include initiatives from Google Quantum AI and Microsoft Quantum, where optimization and machine learning applications are being tested.

Automating code generation for quantum computing

One of the biggest challenges of quantum computing is that specialized knowledge is required to develop quantum algorithms. However, with the help of generative AI, companies can automate code generation for quantum computing. Instead of requiring experts, managers and analysts can simply describe the problems to be solved in natural language and AI tools will then generate the required quantum algorithms. This automation makes the possibilities of quantum computing generally accessible, so that companies can provide modern solutions even without a high learning curve or special expertise.

The combination of AI and quantum computing for automated code generation is an exciting field of research. IBM has also developed Qiskit Code Assistant, a quantum computing platform based on open source. Solutions like this can make it easier for companies to get started with the new technology and enable faster innovation in the field of quantum AI.

Why companies should start using quantum computing now

For managers, quantum computing has a significant and strategic impact on the use of generative AI. Quantum computing will not replace existing AI infrastructures in the short term, but it will play an increasingly important role in next-generation AI models. More and more providers are bringing quantum computing solutions to the market. Therefore, companies that are already looking at quantum-optimized AI today and investing in this technology early on can secure a leadership position.

Why should executives act now?

• Be one step ahead of the competition: Leading technology companies are investing heavily in quantum AI research. As these technologies mature, companies that don’t take precautions to leverage quantum technologies could be left behind (source: Forbes).
• Unlocking new opportunities: Quantum computing has the potential to fundamentally change entire industries such as logistics, pharmaceuticals, finance and cybersecurity. From supply chain optimization and faster drug discovery to better financial risk modelling, quantum-optimized AI can provide new tools for more efficient problem solving (source: McKinsey).
• Enabling long-term efficiency gains and cost reductions: By introducing quantum-optimized AI, companies can improve energy efficiency, accelerate time-to-market for products and mitigate operational risks. As a result, they benefit directly from sustainable competitive advantages (source: World Economic Forum).
• Investing in talent and skills: Quantum computing expertise is currently still niche knowledge, but leaders will need to invest in upskilling their teams in the future. Building a workforce that is familiar with the use of both AI and quantum computing will be crucial for long-term success.

Quantum-optimized AI is not science fiction, but is quickly becoming a strategic success factor. Although it will still take some time before quantum technologies are fully integrated into AI, the course is already being set today. Leaders who recognize the potential of quantum computing and test hybrid models today will set their companies up for success in an AI-powered future.

Quantum computing holds tremendous opportunities for innovation and competitive advantage. By equipping themselves for this change, companies are opening up previously unimaginable possibilities that will change entire industries from the ground up and lay the foundation for growth with AI.

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Further links

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Photo: SAP