Modern quantum computing systems are noisy, remotely-hosted resources that have enabled experimentation but are currently 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 emerging era of quantum processing, 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. Specifically, we will present a hands-on tutorial demonstrating performant classical simulation of quantum workflows, highlighting the functionality of cuQuantum’s state vector and tensor network libraries, cuStateVec and cuTensorNet. Prerequisite(s): Familiarity with Python Familiarity with basic quantum computing concepts Familiarity with popular pythonic quantum computing frameworks like Cirq, Qiskit, or PennyLane.
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