Now that AI has entered the business world, I believe the next stage involves combining AI technology with significantly greater computing power. Quantum AI represents the next step, opening up exciting prospects for organizations by enabling speed, precision, efficiency and innovation in areas where classical technology reaches its limits.
Traditional AI models excel at making predictions based on historical data, but they typically focus on one outcome at a time. When complex, high-dimensional relationships emerge, classical architectures struggle with scale and performance.
Quantum AI approaches these challenges differently. Using principles such as superposition and entanglement, quantum systems can represent and evaluate many possible states simultaneously. This means that companies can not only predict the most likely future but also think through several scenarios in parallel – for example, in areas such as manufacturing, logistics, materials research or risk analysis.
Quantum AI introduces a new quality of probability-based decision intelligence that surpasses the limits of classical AI.
How quantum principles enhance AI
Superposition: Simultaneity of states
In classical computing, a bit is either 0 or 1. A quantum bit, or qubit, can be 0 and 1 at the same time. This property, known as superposition, allows a quantum computer to process multiple possible states simultaneously.
For AI workloads, this means significantly more efficient computation for complex problems, enabling organizations to run more scenarios and evaluate more hypotheses in far less time.
Entanglement: Connected information across dimensions
Entanglement links two or more qubits so that their states are correlated regardless of physical distance. For Ai, this enables richer information exchange, multidimensional pattern recognition and more powerful modeling of relationships that would be computationally expensive or infeasible with classical architectures.
Together, superposition and entanglement unlock new modeling capabilities that support broader exploration of the solution space – not a binary path from input to output.
Cognitive reinforcement, not just automation
Classical AI is often equated with automation, i.e., used to take over routine human tasks; quantum AI, on the other hand, opens up new possibilities for the expansion of human intelligence.
Quantum algorithms are inherently nonlinear and work with probabilities. This enables:
- Faster hypothesis testing (which production configuration yields the highest yield?).
- Multidimensional view of data (instead of one-dimensional optimization).
- Better data-driven decision support reduces unfounded, gut-based decisions.
In short, quantum AI is not just a new tool, but has the potential to create a quantum leap in modeling complex problems.
Bridging the gap between classical AI and the quantum world
Quantum AI is not currently a plug-and-play tool. For hardware, algorithms, and governance to work together effectively, several adjustments are necessary.
1. Hybrid analytics architecture
To optimize both performance and quality, data science workflows need to integrate quantum-capable modules. Integrated analytics platforms allow parts of workflows to run traditionally, while others (for example, optimization sub-steps) are outsourced to quantum backends. Therefore, companies do not need to overhaul their entire IT infrastructure; instead, they can gradually integrate quantum technology.
2. Low-threshold quantum workbenches
Tools like SAS Viya Workbench make it easy for developers, data scientists, and subject matter experts to get started with quantum AI – through familiar environments and languages. According to a recent study, three out of five executives are experimenting with quantum AI or investing in pilot projects, and a workbench supports these initial steps.
3. Hardware access partnerships
Collaborations with leading providers of quantum hardware, for example, regarding quantum annealing technology, open up the possibility for companies to test hybrid scenarios in concrete terms. Annealing is an optimization process for finding the global minimum of a given objective function over a given set of candidate solutions. SAS partners with companies like D-Wave Quantum, IBM, and QuEra Computing to develop hybrid quantum-classical solutions.
4. Governance and business logic framework
An often-neglected aspect: Decisions based on AI must be explainable, trustworthy and integrable into existing business processes – this also applies to quantum AI. An IT platform that integrates AI algorithms with Quantum technology, as well as robust and trusted governance, enables organizations to implement applications that are compliant, trustworthy, and fair within their business processes.
Concrete quantum AI benefits for companies
Across various industries, quantum AI provides advantages in a wide range of areas.
- Optimization of complex problems: Tasks such as production planning, vehicle logistics, energy management or portfolio optimization have extremely large solution scopes. Quantum methods, such as quantum-based optimization (utilizing quantum annealers and hybrid algorithms), can address these problems much faster or more efficiently than classical heuristics – saving time and costs in areas like production and supply chain management.
- Material and molecule simulations: Chemical, pharmaceutical and materials research benefit from more precise simulations on quantum hardware, including faster drug discovery in drug design or material design for batteries. This can shorten development cycles and reduce the need for expensive experiments.
- Machine learning models: Approaches accelerated by quantum technology can improve accuracy or training time for certain tasks (such as feature mapping, kernel methods, or sampling) — especially in data-intensive, complex domains.
- Risk and security simulations: Financial services, insurers, and critical infrastructure can better model risk and make more informed real-time decisions with faster, more realistic simulations.
Case study: A global consumer goods company
In a pilot project, the global consumer goods company has reduced computing time for highly complex problems by 97 percent with the help of SAS Optimization and innovative quantum-based algorithms.
This company faces the challenge of modeling the ingredients for hundreds of product variations that can change constantly in response to market demands. In total, there are 10,114 possible combinations of ingredients, product variations and stored rules for them, more than the number of atoms around the universe. Shorter computing time means higher productivity: Different modeling approaches can be tested in significantly less time, ultimately leading to a faster optimal solution.
In addition to saving time on model training, the company achieves other benefits with quantum AI, including minimizing energy consumption for high-dimensional models, an expanded learning effect by simulating a larger number of scenarios and higher-quality results. The project demonstrates that a hybrid approach combining quantum technology and classical AI is best suited to generate an accurate simulation while optimizing resource utilization.
Tips for getting started with quantum AI
- Define pilot use cases: Identify processes where classic optimization reaches its limits (e.g., production planning, supply chain optimization, material design)
- Develop a hybrid roadmap: Define specific steps for integrating quantum modules into the analytics architecture.
- Building governance: Establishing clear rules on how quantum decisions should be validated, explained and documented, in the spirit of responsible AI.
- Seeking partnership: Docking with existing ecosystems to tap into expertise, infrastructure and funding.
- Building knowledge: Training data science teams for advanced methods in the sense of hybrid quantum AI.
Looking ahead to the future of quantum AI
Quantum AI marks the step from conventional data analysis to much stronger, decision-oriented intelligence. By combining classical AI methods with the computing power of quantum technology, probabilistic decision modeling gains considerable speed and precision. Modern analytics platforms, complemented by robust governance frameworks, can create an environment where pragmatic solutions drive real innovation beyond mere hype.
