Demystifying Quantum Machine Learning with Luis Serrano, PhD (Zapata Computing)

Synthetic Intelligence Forum is excited to convene a session about “Demystifying Quantum Machine Learning” with Luis Serrano, PhD (Quantum AI Research Scientist at Zapata Computing). Topic: Quantum computers are known to be efficient for factoring numbers. They can efficiently simulate…

Demystifying Quantum Machine Learning with Luis Serrano, PhD (Zapata Computing)

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Synthetic Intelligence Forum is excited to convene a session about “Demystifying Quantum Machine Learning” with Luis Serrano, PhD (Quantum AI Research Scientist at Zapata Computing).

Topic: Quantum computers are known to be efficient for factoring numbers. They can efficiently simulate solid-state compounds and molecules, enabling the design of new chemicals, materials and drugs. Quantum scientists have also built heuristics for solving hard instances of optimization problems.

Most experts predict that within the next few years we will have a large enough quantum computer to enable an advantage for some real business problems. However, it is still unclear exactly which problem it will be. Most expect that quantum advantage will first be achieved in one of three areas: machine learning, simulation of quantum materials (e.g. chemistry), or optimization.

Today’s fully quantum computers and quantum simulators are large enough to explore potential use cases by integrating them with classical computers. While hybrid solutions might demonstrate quantum advantage, most applications will more likely require additional hardware development before this is possible.

Quantum-inspired solutions, on the other hand, make it possible to solve larger instances. These solutions, though lacking the full power of quantum computing, approach problems in a similar way and may offer advantages over legacy methods. In addition, their design offers a straightforward translation to fully quantum computers and makes them a useful window into their performance.

In this talk, Luis will describe breakthrough advances at the intersection of quantum computing and machine learning. He will explain the opportunities and challenges related to applying the power of quantum computing to machine learning workloads.

Biography: Luis Serrano did his PhD in mathematics at the University of Michigan and his bachelors and masters in mathematics at the University of Waterloo.

After some years as a postdoctoral fellow at the University of Quebec, he moved to industry, where he worked at Google as a machine learning engineer, at Udacity as the head of AI content, and at Apple as a lead AI educator.

He is the author of the book Grokking Machine Learning and maintains a popular YouTube channel where he explains machine learning in layperson terms: Serrano Academy – Machine Learning and Math Made Easy.

Profiles of the host and presenter:
• Vik Pant, PhD – https://www.linkedin.com/in/vikpant
• Luis Serrano, PhD – https://www.linkedin.com/in/luisgserrano

Web resources from Luis Serrano, PhD:
• Grokking Machine Learning – https://www.manning.com/books/grokking-machine-learning
• YouTube – http://www.youtube.com/c/LuisSerrano
• Serrano Academy – https://serrano.academy/
• Udacity – https://www.udacity.com/blog/author/luis-serrano
• Zapata Computing – https://www.zapatacomputing.com/team/luis-serrano/

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• Website – http://www.synthint.ai
• LinkedIn (Page) – https://www.linkedin.com/company/synthint/
• LinkedIn (Group) – https://www.linkedin.com/groups/12092618/
• YouTube – https://www.youtube.com/c/SyntheticIntelligenceForum

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