SAS Brings Quantum Computing to Businesses Before Hardware Maturity
Quantum computing has a long-term horizon. In its mature form, characterized by stable hardware suitable for large-scale production use, experts place its arrival around 2030. For companies, this has so far meant a waiting game: the tangible benefits of the quantum paradigm may only arrive at that point.
SAS chooses not to wait for that deadline, and at Innovate 2026, it proposes a middle path built around a concept until now largely confined to specialized literature: quantum AI, which means running machine learning algorithms on already available quantum hardware in a hybrid mode with classical computing. It does not replace pure quantum computing but anticipates it, enabling immediate tackling of specific problems such as complex pattern recognition in financial fraud, molecular simulation for pharmaceutical research, or optimization of telecommunications networks.
Assessing how ready the market is to look in this direction is research conducted by the company on over 500 global leaders, presented at Innovate 2026. The most significant data point concerns the nature of the obstacle stated by the interviewed leaders: at the top of their concerns is not the cost of implementation but the uncertainty about real use cases. Companies want to understand where quantum AI produces results that classic alternatives cannot deliver before committing significant investments.
A Comparative Environment Between Classical, Quantum, and Hybrid
On this consideration, SAS introduces its own product, set to be released in Q4 2026 for customers using Viya: SAS Quantum Lab. This environment compares the results obtained with classical, quantum, and hybrid computing on the same use case, allowing organizations to choose the approach based on the quality of the result and cost. In tests conducted by the company, the lab showed performance up to 100 times higher than classical computing in certain workloads, with cost reductions of up to 99%. The numbers are indicative and relate to specific scenarios, and should be treated as laboratory milestones rather than average performance.
One of the most explicit product choices concerns accessibility. Quantum Lab is aimed not only at teams with a quantum physics expert on staff but also seeks to open experimentation to data scientists and AI leads who want to evaluate the technology without needing hard-to-find specialized skills. To facilitate this, SAS introduces a virtual tutor that answers questions, provides sample code, and suggests next steps, lowering the entry threshold.
Amy Stout, Head of Quantum Product Strategy at SAS, presents the tool as a testing ground: "Companies are interested in using quantum technology, but the barriers to entry have been too high, and this requires a concrete response. Quantum Lab is a practical experimentation environment where one can learn and innovate with the goal of achieving a concrete ROI."
Use Cases Companies Want to Address
The conclusions of the research outline a consistent application perimeter with SAS's historical customer base. Among the cited objectives are the detection of complex patterns in financial fraud, real-time optimization of traffic on 5G networks, molecular simulation for drug identification, optimization of logistic chains, predictive modeling of customer behavior, and training large language models with reduced time and resources. These are the sectors where spending on classical computing is currently most concentrated, and where the difference between an adequate solution and an insufficient one is immediately visible on the balance sheet.
Bill Wisotsky, Principal Quantum Architect at SAS, summarizes the company's approach: "Companies of all sizes want to build their own intellectual property on quantum technology, their own path towards this paradigm, so they are prepared when it reaches full maturity. At the same time, they proceed cautiously because they do not want to commit to expensive hardware investments without certainty that it will yield results. SAS attempts to level the playing field by identifying real use cases even today."
In terms of positioning, SAS occupies a space different from hyperscalers that offer direct access to quantum hardware. Quantum Lab does not sell computing power; it sells methodology: structured comparison, real use cases, a reasoned learning curve for enterprise teams that currently do not have a quantum physicist on staff. For readers of the results, it remains important to keep three levels separate. The first is the proven data, or what emerges from research and measured performance in specific tests. The second is the product intent, i.e. the laboratory's announcement availability for the fourth quarter and the features still in the design phase. The third is the market vision, i.e. the industrial leap of quantum computing expected in the coming years. These are three levels that should never be confused with one another.