Hybrid Quantum-Classical Computing with NVIDIA CUDA-Q (Part 2)

The CUDA-Q Platform for hybrid quantum-classical computers enables integration and programming of quantum processing units (QPUs), GPUs, and CPUs in one system. This workshop will cover an introduction to quantum computing with CUDA-Q, simulating quantum circuits, noise modeling, and variational quantum algorithms.


Learning Objectives: By the end of this workshop participants will:

Gain hands-on experience simulating quantum circuits on CPU and GPU
Experiment with time-dependent quantum dynamics, noise modeling, qubit coherence properties, and calibration
Build a variational quantum algorithm using a classical optimizer to train a parameterized quantum circuit
Understand how to scale circuit simulation and optimization over multiple GPUs and QPUs

Prerequisites: Basic Python programming and linear algebra. Exposure to quantum computing fundamentals and programming frameworks beneficial but not required.

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Co-hosted with: GradFUTURES

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