Quantum computers have received extensive publicity over the last 20 years, promising potentially major performance gains over classical computers for a range of industrial problems. However, while progress is being made, questions remain about how to translate the underlying theory into practical computers. The resulting debate between quantum enthusiasts and quantum skeptics can be difficult to navigate, especially for those who have little or no familiarity with the underlying concepts.

This tech talk will not take sides in the debate, nor make predictions about quantum computing capabilities of the future. Rather, it will try to present the technical details of quantum computers in a way that resonates with mathematical optimization practitioners. It’s easy to overlook the connections between classical mathematical optimization concepts and quantum computing. However, many mathematical optimization concepts can offer guidance for solving challenging problems in a quantum computing environment.

Quantum computers fall into two categories: quantum annealers and logic gate quantum computers. Part I of this presentation will focus on quantum annealers, as the connections to classical optimization are more direct.

Stayed turned for Part II, planned for late July, where we’ll consider the logic gate architecture and the underlying linear algebraic concepts that are familiar to many optimization users.