Even when the transistor today is governed by quantum mechanics, its computational functionality can be described through classical mechanics. As the need to sustain Moore’s Law pushes for higher scale integration within circuits, the size of transistors now pushes into sizes where quantum effects cannot be avoided, thus breaking this 80-year paradigm.

Simultaneously, it became apparent that quantum mechanical systems can be used to simulate other physical systems in the most natural way possible. Hence, quantum computing is both possible and necessary going forward. During this talk, we will undergo an overview of quantum computing from the perspective of markets, principles, systems, applications and strategic aspects decision makers should be aware of as quantum markets and technologies evolve.

Presented By: Santiago Núñez-Corrales, Ph.D. Quantum Lead and Research Scientist at the National Center for Supercomputing Applications Senior Research Staff at the Illinois Quantum Science and Technology Center and the Hybrid Quantum Architectures and Networks NSF QLCI, University of Illinois Urbana-Champaign.

His work concentrates on the development of theory, technologies and practice that draw from computing, information science and statistical physics to accelerate the maturation of Noisy Intermediate-Scale Quantum systems of today toward future scales of utility. In addition, Dr. Núñez-Corrales uses the same toolbox to characterize intelligent systems from the a physics-based perspective beyond mainstream ML, and thus inform the construction of agent-based models to study global risk.

Dr. Núñez-Corrales received his doctorate in Informatics with a minor in Global Studies at the University of Illinois Urbana-Champaign, and prior his Bachelor’s degree in Computer Engineering from the Costa Rica Institute of Technology. His experience includes science and technology foresight and public policy, as well as embedded systems engineering. He has been awarded with multiple recognitions, including the 2017 ACM/Intel SIGHPC Computational and Data Science Fellowship.