Accelerating Scientific Computing Workflows through GPU-based Quantum-classical Programming, Compilation, and Simulation

Sunday, May 29, 2022 2:00 PM to 6:00 PM · 4 hr. (Europe/Berlin)
Hall Y12 - 2nd Floor
AcceleratorsEducation & TrainingMathematical LibrariesQuantum Computer Simulators


Modern quantum computing systems are noisy, remotely-hosted resources that have enabled experimentation but are incapable of application-specific quantum advantage. Research and development activities promise to considerably advance this situation, and we are starting to observe quantum-classical systems with less noise and tighter coupling between CPU and quantum processing resources, enabling dynamic circuit execution based on qubit measurement readout. In this new era of quantum coprocessing, the community uniquely requires robust circuit simulation technologies for debugging and verification as well as for quantum applications research. This tutorial will introduce participants to the NVIDIA solution for GPU-accelerated quantum circuit simulation, cuQuantum, embedded in several of the leading quantum circuit simulation frameworks, as well NVIDIA’s architecture for quantum-classical computing. Specifically, we will present a hands-on tutorial demonstrating performant classical simulation of quantum workflows, highlighting cuQuantum’s state vector and tensor network libraries, cuStateVec and cuTensorNet. In addition, we will discuss NVIDIA’s hybrid quantum-classical programming model.

Important information:

o Bring your own laptop

o Create or log into your NVIDIA Developer Program account ( This account will provide you with access to all of the DLI training materials during and after the workshop. Note: The course material will not be available in your account before the start of the workshop.

o Visit and make sure all three test steps are checked “Yes.” This will test the ability for your system to access and deliver the training contents. If you encounter issues, try updating your browser. Note: Only Chrome and Firefox are supported.

  • Alex McCaskey (NVIDIA)
  • Christian Hundt (NVIDIA)
  • Harun Bayraktar (NVIDIA)
  • Andreas Hehn (NVIDIA)
  • Jin-Sung Kim (NVIDIA)
  • Sam Stanwyck (NVIDIA)