Quantum software and orchestration provider QCentroid has introduced an enterprise hybrid application design framework within its QuantumOps platform, integrating NVIDIA CUDA-Q to automate the placement of quantum components within existing classical high-performance computing (HPC) software stacks. Implemented in partnership with Gradiant and the Galician Supercomputing Center (CESGA) under the QATALIZE project, the workflow establishes an evidence-driven methodology to define, benchmark, and optimize classical-quantum boundaries across enterprise AI, simulation, and generative modeling applications.
The operational framework structures hybrid integration into three granular decision levels: component selection (identifying target modules within complex software pipelines), boundary placement (determining the precise insertion layer for parameterized quantum circuits within deep neural architectures), and execution resource assignment. Demonstrated on a conditional Generative Adversarial Network (cGAN) targeting catalyst materials discovery, the platform evaluates alternative hybrid Generator designs—ranging from early latent space quantum transformations to compressed quantum bottlenecks—against a classical PyTorch baseline running on CESGA’s HPC clusters and 32-qubit Qmio superconducting QPU infrastructure.
| [ QCentroid QuantumOps & NVIDIA CUDA-Q Enterprise Hybrid Cycle ] | ||
|---|---|---|
| Architectural Stage | Software & Infrastructure Stack | Operational Metrics & Output |
| Baseline & Modeling | • Classical PyTorch & HPC Solvers • QuantumOps Expert AI Agents | • Candidate Validity & Novelty Baselines • Standardized Use-Case Pack Generation |
| Boundary Engineering | • Interleaved Parametrized Quantum Circuits (PQCs) • NVIDIA CUDA-Q & PyTorch Bindings | • Latent Space & Bottleneck Layer Routing • Multi-Level Architecture Comparison |
| Execution & Validation | • GPU-Accelerated cuQuantum Simulators • CESGA Qmio 32-Qubit Superconducting QPU | • DFT-Validated Target Hit Rate • Noisy Simulation & Hardware Resource Benchmarks |
By decoupling application code from hardware execution targets via CUDA-Q’s unified backend, QuantumOps leverages domain-specific AI agents to manage multi-variable experiment matrices across GPU-accelerated simulation (cuStateVec/cuTensorNet), noise-aware modeling, and physical QPU execution. The framework evaluates hybrid performance using multi-dimensional criteria—including chemical validity rates, candidate novelty/uniqueness, Density Functional Theory (DFT) hit rates, and total execution costs—providing an auditable baseline to justify whether quantum acceleration should be integrated or kept classical.
Review the technical update on QCentroid Blog here.
September 14, 2026
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