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NVIDIA CUDA-Q for Quantum Developers: Build Hybrid Quantum-Classical Applications with Python & C++ for GPU-Accelerated Simulation and Real QPUs
Paperback
Publisher: Independently Published
Published: Oct 18 2025
Pages: 276
Weight: 1.06
Height: 0.58 Width: 7.00 Depth: 10.00
Language: English
NVIDIA CUDA-Q for Quantum Developers: Build Hybrid Quantum-Classical Applications with Python & C++ for GPU-Accelerated Simulation and Real QPUs
Quantum computing is no longer a distant theory, it's here, blending with classical systems to solve problems that once seemed unreachable. Yet, most developers struggle to connect quantum logic with real-world, GPU-accelerated workflows. NVIDIA CUDA-Q for Quantum Developers bridges that gap with a hands-on, engineering-first approach that shows you exactly how to build, simulate, and deploy hybrid quantum-classical applications in Python and C++ using NVIDIA's CUDA-Q platform.
This book equips you to think and build like a modern hybrid developer. It takes you from your first working kernel to high-performance variational algorithms, seamlessly integrating quantum kernels with classical optimizers, tensor-network simulators, and GPU acceleration. Every concept is backed by complete, runnable examples and real code you can execute directly, no placeholders, no theoretical shortcuts.
Through clear explanations and structured examples, you'll learn how to:
Write and execute quantum kernels using Python and C++ APIs.
Run GPU-accelerated quantum simulations with cuQuantum integration.
Implement practical algorithms like VQE, QAOA, and Grover's search.
Build hybrid optimization loops with classical frameworks such as SciPy.
Profile performance, manage backends, and prepare for execution on real QPUs.
Containerize, test, and deploy reproducible workflows in professional environments.
Every chapter builds toward production-grade patterns, teaching you how to debug, test, and scale your circuits while maintaining the performance discipline expected of GPU developers. Beyond syntax, it focuses on design logic: how to think about hybrid algorithms, measure efficiency, and engineer robust quantum-classical systems for simulation and hardware.
Whether you're a software engineer entering quantum computing for the first time or an experienced researcher looking to optimize multi-GPU simulations, this book provides a practical framework for bridging both worlds.
If you're ready to turn quantum theory into working code, and see your circuits run faster, smarter, and closer to hardware, NVIDIA CUDA-Q for Quantum Developers is your next essential step.
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