A Path to Practical Quantum Advantage with Quantum Machine Learning Models

DISI Seminars
22 March 2022
Start time 
3:00 pm
on Zoom platform
Department of Information Engineering and Computer Science
Target audience: 
University community
Online – Registration required
Registration deadline: 
22 March 2022, 13:00
Contact person: 
dr. Davide Pastorello, Department of Information Engineering and Computer Science
Alejandro Perdomo Ortiz (Zapata Computing. Toronto, Canada)


Practical quantum advantage, the demonstration of a quantum or quantum-assisted model solving a valuable academic or industry interest problem faster, better, or more cost-efficient than any classical algorithm, is the most sought-after milestone after the recent results on quantum computational advantage with random quantum circuits. Besides quantum chemistry, where a more explicit path is laid out for achieving quantum advantage, machine learning (ML) and combinatorial optimization problems (COP) stand out as key candidates. Despite all the efforts, there is still no demonstration of quantum advantage for practical and industrial applications in ML and COP.
This talk will focus on generative modeling, a probabilistic unsupervised ML application, and combinatorial optimization as an application domain to achieve a practical quantum advantage in the near term. We will discuss the challenges ahead and how quantum ML can help accomplish this goal in the light of recent progress in quantum algorithms and metrics, which allow tracking progress towards that milestone.

About the speaker

Alejandro is the Research Director of Quantum AI at Zapata Computing. Toronto, Canada.
He is a senior research scientist exploring the computational limits and opportunities of quantum computing for problems in artificial intelligence. Before joining Zapata Computing, Alejandro was the lead scientist of the Quantum Machine Learning effort at NASA's Quantum Artificial Intelligence Laboratory (NASA QuAIL). He was also the Co-Founder of Qubitera LLC, a consulting company acquired by Rigetti Computing where he worked after NASA and before his current appointment as the Associate Director of Quantum AI at Zapata Computing. He also holds an Honorary Senior Research Associate position at University College London.

Alejandro did his graduate studies, M.A and Ph.D. in Chemical Physics, at Harvard University. Over the past 10+ years, he has worked on the implementation of quantum computing algorithms, enhancing their performance with physics-based approaches while maintaining a practical, application-relevant perspective. His latest research involves the design of hybrid quantum-classical algorithms to solve hard optimization problems and intractable machine learning subroutines.
While at NASA, he was the recipient of the 2014 Staff Appreciation and Recognition (STAR) Award from the University of California, and the 2016 Ames Honor Award in the category of Contractor Employee. In 2017 he was recognized by the Colombian Embassy in the USA as one of the top 22 most influential Colombians in the USA, for his scientific leadership and outreach to help young generations interested in science and technology.