BILBAO, SPAIN, March 16, 2026 -- QCentroid, the enterprise QuantumOps platform for orchestrating and integrating quantum and hybrid optimization workflows, today announced that NVIDIA CUDA-Q is now integrated and available within the QCentroid platform. The capability enables enterprise teams to develop and evaluate hybrid quantum-classical workflows using NVIDIA accelerated computing alongside quantum backends, with a structured approach to experimentation, benchmarking, and lifecycle management.

QCentroid has been part of the NVIDIA Inception program since 2024, and this release extends QCentroid’s mission: helping organizations navigate the “pre-quantum advantage” era through disciplined hybrid experimentation, strong baselines, and reproducible results,  while remaining ready to scale adoption as quantum hardware evolves.

“Enterprise adoption doesn’t happen through one-off demos, it happens through repeatable workflows, governance, and credible benchmarking,” said Carlos Kuchkovsky, CEO of QCentroid. “By making CUDA-Q available inside QCentroid QuantumOps, teams can combine GPU-based development and baselining with quantum execution paths in a single operational framework, reducing friction from discovery to experimentation and comparison”.

“Breakthroughs towards quantum-GPU supercomputers are being made when users work in a truly hybrid environment,” said Sam Stanwyck, Director of Quantum Product at NVIDIA. “Through its integration with the CUDA-Q platform, Qcentroid now gives users the GPU acceleration they need to explore tomorrow’s quantum applications today.”

Enabling hybrid execution and benchmarking with CUDA-Q inside QuantumOps

By using NVIDIA CUDA-Q within QuantumOps, enterprise teams can leverage GPU-accelerated simulations of quantum computing, streamline execution pipelines and reduce friction when running quantum and hybrid workflows as part of broader experimentation and benchmarking cycles.

This enables organizations to shorten time-to-learning and accelerate iteration loops, one of the most persistent bottlenecks in real-world quantum adoption, by providing a consistent operational layer for:

• Hybrid workflow orchestration
• Benchmark design and execution cycles
• Comparative analysis across approaches and configurations
• Traceability and reporting for enterprise decision-making

Agentic vision for quantum adoption: AI-assisted workflow acceleration

To help enterprises enter the benchmarking loop faster, QCentroid complements CUDA-Q execution enablement with an AI Copilot layer (Quantum expert agents) that converts early exploration into an execution-ready use-case pack: a full use-case definition, initial model formulation, required data schema, recommended solver and backend paths, and starter solver code, to speed up the most resource-intensive steps of the adoption lifecycle.

By combining CUDA-Q support, orchestration, benchmarking, and agentic workflows, QuantumOps enables organizations to build a structured, evidence-based adoption pathway, from exploration to measurable progress.