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Quantum Machine Learning With TensorFlow Quantum


Quantum machine learning is at the crossroads of two of the most exciting current areas of research: quantum computing and classical machine learning. It explores the interaction between quantum computing and machine Learning, investigating how results and techniques from one field can be used to solve the problems of the other. With an ever-growing amount of data, current machine learning systems are rapidly approaching the limits of classical computational models. In this sense, quantum computational power can offer advantage in such machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Goggle’s open source framework, TensorFlow Quantum (TFQ) ideally facilitates rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. In this talk I provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks.
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Senior Executive with 25+ years of experience creating, scaling business growth and technology transformation at quantum computing, voice and digital solution start-ups, public and private companies. Technology Innovator who leads the development and monetization of products to complex problems in business and society through Quantum Computing, Big Data Analytics, Cloud, AI DeepStack, Machine Learning, Mobility and IoT. High-Integrity Leader with success building and inspiring global business, technology, and client-facing teams; recruiting proven executives; and attracting experienced Board members.