Classiq Accelerates Hybrid Quantum Application Development and Execution with NVIDIA CUDA-Q
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Insider Brief Classiq has demonstrated integration between its quantum computing platform and NVIDIA CUDA-Q to accelerate workflows from AI-assisted quantum modeling through execution in hybrid quantum-classical environments, reducing iteration cycles for quantum research teams. Benchmark tests on a financial options-pricing application using Iterative Quantum Amplitude Estimation reduced circuit synthesis and execution time for a 31-qubit circuit from 67 minutes to 2.5 minutes using a single NVIDIA A100 GPU. The integration leverages NVIDIA AI infrastructure for parallelized quantum simulation execution, enabling exploration of large and complex quantum circuits across GPUs, simulators, and emerging quantum hardware within hybrid computing workflows. PRESS RELEASE — Classiq, the leading quantum computing software company, today announced a demonstration of the integration between the Classiq platform and NVIDIA CUDA-Q that accelerates the workflow from high-level, AI-assisted quantum modeling through execution in hybrid quantum-classical environments, improving runtime and iteration speed for quantum research and development teams. The updated integration reduces friction between algorithm design, rapid iteration and execution across heterogeneous compute resources, including GPUs, simulators and emerging quantum hardware. The work helps teams shorten iteration loops, a requirement for testing, benchmarking and refining hybrid approaches as high-performance computing environments evolve. Hybrid quantum-classical computing plays a central role in how organizations evaluate and operationalize quantum-ready methods, especially as quantum workflows increasingly rely on classical acceleration for simulation, preprocessing, optimization loops and orchestration. By tightening the connection between modeling, compilation and execution, Classiq’s integration of CUDA-Q aims to help researchers and developers move faster from intent to runnable experiments and back again. Tests were done on a financial options-pricing benchmark using IQAE (Iterative Quantum Amplitude Estimation) available through the Classiq platform. The benchmark was implemented with the updated Classiq integration and executed via CUDA-Q. Circuit synthesis and completed execution of a 31 qubit circuit was reduced from 67 minutes to 2.5 minutes using a single NVIDIA A100 GPU. The updated integration leverages NVIDIA AI infrastructure to achieve massive parallelization of quantum simulation execution. This enables the exploration of large and complex quantum circuits, helping to ground assumptions regarding quantum scale and quantum algorithms and paving the way for practical quantum utility at scale on emerging quantum hardware. “Practical quantum R&D requires iteration loops that are fast, repeatable and connected to execution,” said Nir Minerbi, co-founder and CEO of Classiq. “This integration with NVIDIA CUDA-Q is designed to help teams move from high-level intent to running experiments faster, so they can test ideas, compare approaches and build toward production-ready hybrid workflows.” “NVIDIA CUDA-Q is designed to help developers build and run hybrid quantum-classical applications across today’s accelerated computing environments and emerging quantum systems,” said Sam Stanwyck, Director of Quantum Product at NVIDIA. “Classiq’s integration of CUDA-Q allows teams to shorten iteration cycles, test ideas more quickly, and evaluate quantum-ready methods in the context of modern HPC pipelines.” Mohib Ur Rehman LinkedIn Mohib has been tech-savvy since his teens, always tearing things apart to see how they worked. His curiosity for cybersecurity and privacy evolved from tinkering with code and hardware to writing about the hidden layers of digital life. Now, he brings that same analytical curiosity to quantum technologies, exploring how they will shape the next frontier of computing. Share this article: