Join Jennifer Fernick— a quantum computer scientist, decade-long AI practitioner, and experienced enterprise technology executive—to learn to filter signal from noise in discussions about quantum machine learning (QML). You’ll discover what a quantum computer is and how it works, explore the types of algorithmic innovations offered by both near-term and large-scale quantum computers, and understand how quantum computers could be combined with the current state-of-the-art in machine learning (ML) algorithms, leading in some cases to more efficient quantum-enhanced ML algorithms or potentially, entirely new fully quantum ML algorithms.
Along the way, Jennifer shares concrete use cases and industries where quantum machine learning is likely to have the biggest impact on science and business and discusses specific opportunities for AI advancement in these areas. She concludes with a timeline for the development of quantum computers, architectural and service model considerations for QML platforms, and a discussion of what quantum machine learning cannot do, illustrating why this technology works, where it is going, what types of problems it can (and cannot) solve, how it will impact organizations, and where and why it matters compared to classical (nonquantum) approaches to machine intelligence.
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