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Quantum computing aspires to deliver more powerful computation and faster results for certain types of classically intractable problems. Quantum circuit simulation is essential to understanding quantum computation and the development of quantum algorithms.
Accelerated computing uses parallel processing to speed up work on demanding applications, from AI and data analytics to simulations and visualizations. NVIDIA cuQuantum is an SDK of optimized libraries and tools for accelerating quantum computing workflows. NVIDIA Quantum-Optimized Device Architecture (QODA) is a first-of-its-kind platform for hybrid quantum-classical computers, enabling integration and programming of quantum processing units (QPUs), GPUs, and CPUs in one system.
In this DataHour, the acceleration of Quantum Computing Simulators using NVIDIA GPUs and QODA will be discussed in detail.
Prerequisites: Enthusiasm for learning Data Science.
Dr. Manish Modani
Principal Solution Architect at NVIDIA
Dr Manish has 17+ years of industry experience in High Performance Computing (HPC) and weather. He has ported, benchmarked, and optimized HPC applications from various domains including Weather, Insurance, Agriculture, Disaster Management, Molecular Dynamics etc. Manish has hands-on experience to work on various HPC architectures including Cray X1E, GPU enabled IBM Power, x86 and Blue gene, CDAC PARAM 10000, PADMA and Siddhi. Manish’s work is patented and published in various peer reviewed research journals.
Manish has worked in Cray for 2 years and in IBM for 10 years. During his PhD from Centre for Atmospheric Sciences (CAS), Indian Institute of Technology (IIT) Delhi, India, Manish has developed air pollution models for the dispersion of air pollutants in low wind conditions. Presently, Manish is working as a Principal Solution Architect in HPC, AI for HPC and Quantum Computing Simulations at NVIDIA.
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