Quantum computers have received extensive publicity throughout the last 20 years, showing potential for major performance improvements over classical computers when it comes to challenging industrial problems. However, while progress is being made, questions remain about how to effectively 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 are new to the underlying concepts.

In Part I of this presentation, we explored the technical details of quantum computers, with a focus on quantum annealers. Now, in Part II, we discuss the logic gate architecture, revealing the underlying linear algebraic concepts that are familiar to many optimization practitioners.

The connections between classical mathematical optimization concepts and quantum computing can get lost in the collection of different concepts and notations associated with the latter. However, a closer look reveals that numerous mathematical optimization concepts can help us learn how to solve challenging problems in a quantum computing environment.

We examine how the entanglement of qubits results in significant computational power, and how that power can be harnessed into algorithms that can potentially solve complex problems much faster compared to a classical computing environment.

There is no prerequisite for this presentation. However, if you are unfamiliar with the concepts behind Prisoners in Hats puzzles, you may find it helpful to explore or review them in advance. This video provides a good overview.