Generalized quantum process discrimination problems

Understand

We study a broad class of quantum process discrimination problems that can handle many optimization strategies such as the Bayes, Neyman-Pearson, and unambiguous strategies, where each process can consist of multiple time steps and can have an internal memory.

• Given a collection of candidate processes, our task is to find a discrimination strategy, which may be adaptive and/or entanglement-assisted, that maximizes a given objective function subject to given constraints.
• Our problem can be formulated as a convex problem.
• Its Lagrange dual problem with no duality gap and necessary and sufficient conditions for an optimal solution are derived.