Hybrid quantum computing for enterprises: how SAS is making the future accessible
- Marc Griffith
- 1 minute ago
- 5 min read
Summary SAS lanches Quantum Lab on Viya to bring hybrid quantum computing to enterprises: a toolkit arriving in Q4 2026 with classic/quantum/hybrid comparisons, accelerations up to 100x, estimated cost savings of 99%, and a virtual tutor to reduce the learning curve. Key takeaways
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Hybrid quantum computing for enterprises is SAS's stated objective with the new Quantum Lab, a toolkit designed to make capabilities previously reserved for specialized laboratories accessible to data scientists and business analysts. The toolkit will be available on SAS Viya by the fourth quarter of 2026 and includes tools for comparison, optimization, and a virtual tutor to accelerate adoption.
Why quantum matters to businesses
Complex optimization problems, such as the traveling salesman problem with hundreds of destinations, quickly become intractable for classical computers. Quantum systems, in hybrid architectures, explore the solution space in parallel, offering potentially substantial reductions in compute times.
Quantum Lab: what it offers and how it works
Presented at the SAS Innovate 2026 conference in Grapevine, Texas, Quantum Lab is not just an API: it is a suite that lets you test classical, quantum, and hybrid approaches on the same business use cases. The first feature enables side-by-side comparisons of results produced by different approaches to identify the most effective solution for a given problem.
The second set of features focuses on performance optimization: SAS's internal tests show accelerations of more than 100x and cost savings of up to 99% versus traditional solutions. These figures, if confirmed on real-world cases, could transform quantum from an experimental curiosity into a concrete competitive lever.
Comparing classical, quantum, and hybrid systems on the same dataset is essential to understand where quantum adds tangible value and where it is limited.
The lab's third function is a quantum AI virtual tutor that provides answers, code examples, and practical guidance for the next steps in learning. The tutor lowers the barrier to knowledge, turning costly experiments into low-risk learning paths.
Integration with Viya and concrete use cases
SAS aims to integrate the lab directly into Viya, its platform for data scientists and enterprise analysts, so that quantum optimization tools can be called from existing production workflows. The goal is to let teams evaluate quantum's value without needing to redesign the entire data pipeline.
Industries identified as immediately benefiting include fraud detection on complex patterns, traffic optimization in 5G networks, acceleration of molecular simulation for pharmaceutical R&D, optimization of supply chains, and improvement of ML workflows for customer profiling. These are examples of business problems that share hard mathematical structures for purely classical methods and where quantum can emerge as a practical solution.
To understand quantum's potential it's crucial to map business use cases with combinatorial or optimization structure and measure their value against integration costs.
Adoption data and market perception
A SAS survey of more than 500 global companies shows a shift in adoption barriers: in 2025 the main obstacle was cost, but in 2026 uncertainty about real-world uses now leads, followed by cost and skills gaps. Today the fear of investing without a clear practical direction is the primary brake on quantum adoption.
This evidence indicates that tools like Quantum Lab, designed to lower the learning curve and offer comparative proofs, address a real market need. The low-cost exploration and the ability to avoid non-transferable results to production are essential for maturing corporate initiatives.
AI agentic governance and orchestration
Parallel to quantum, SAS accelerates AI agentic capabilities: the proposed architecture is based on shared fundamentals of governance, security, monitoring, and orchestration, also embodied by the new SAS Viya MCP Server. This infrastructure enables Viya's ML models to be used by external agents, including third-party LLMs, while maintaining risk mitigation.
The main organizational risk is agent sprawl—the uncontrolled proliferation of automated agents within the company. To address this, SAS has introduced AI Navigator, a governance tool designed to control agents before wide-scale adoption. Preventive governance is crucial to avoid vendor lock-in and ambiguity around responsibilities and automated decisions.
Architectural choices and latency
Not all business decisions need to go through an LLM: in high-speed contexts, such as banking fraud detection with a decision window under 30 milliseconds, classic ML models remain preferable for their latency. The architecture must be able to select the right tool based on performance constraints and not apply AI agentic by default.
Critical debate: opportunities and limits for startups and innovators
The launch of Quantum Lab opens an important window for startups and R&D departments: having access to an environment that compares different approaches reduces exploration risks and enables low-cost experimentation. For startups, this capability can accelerate the validation of business models based on optimization, molecular simulation, or complex matching.
However, technological and supply chain limits remain: quantum hardware is still evolving and suppliers are seeking production stability. Startups should thus balance quantum exploration with concrete integration plans and fallback to classical technologies.
Another critical point concerns skills: the shortage of qualified personnel hinders wide-scale adoption and makes valuable tools that provide practical training and automate the most specialized part of the experiment. Investing in internal training and partnerships with providers that offer tutors and open-source accelerators can be decisive for scaling.
Finally, there's the issue of return on investment: many companies fear spending without knowing whether quantum will deliver a concrete advantage. Hybrid approaches and tools that measure benefits in real-world scenarios are the most effective antidote to hype and for more solid investment decisions.
Practical practices for those starting out
For those leading a startup or an innovation team, we suggest the following steps: map business problems with a combinatorial structure, experiment in hybrid environments with direct comparisons, invest in small repeatable proofs of concept, and integrate governance from the outset. This road map reduces the risk of unproductive investments and helps determine where quantum can truly offer a competitive edge.
Engaging technology partners, using open-source resources (such as the agentic accelerators already released by SAS on GitHub), and defining clear production metrics (latency, throughput, cost per operation) allow for a rapid transition from experimentation to production when results justify it. Shared metrics and acceptance criteria are essential to avoid false promises and non-transferable results.
Timelines and outlook
Most experts expect quantum in production in the early years of the next decade; SAS aims to give those who start now a head start, arguing that the advantage won't go to those who wait but to those who arrive prepared. Quantum Lab should function as a form of insurance: exploring now at low cost to be ready for the competitive shift.
Resources and contacts
The project is led by SAS insiders such as Bryan Harris (CTO) and Amy Stout (product strategy quantum), with leadership on AI agentic from Marinela Profi. Knowing the teams and roadmaps of providers helps plan partnerships and evaluate technological integrations.
A practical tip for founders
If your startup faces optimization, simulation, or large-scale matching problems, promptly consider a hybrid proof-of-concept and plan clear value metrics. Starting with comparative tests on Viya or hybrid environments will give you concrete data for sound investment decisions.
Where to move next
The real differentiator will not be the quantum technology alone, but organizations' ability to integrate experimental tools into governed and measurable workflows. Those who succeed at combining governance, training, and practical experimentation will gain a sustainable advantage in the next technology cycle.