  Quantum Bridge Analytics relates to methods and systems for hybrid
classical-quantum computing, and is devoted to developing tools for bridging
classical and quantum computing to gain the benefits of their alliance in the
present and enable enhanced practical application of quantum computing in the
future. This is the second of a two-part tutorial that surveys key elements of
Quantum Bridge Analytics and its applications. Part I focused on the Quadratic
Unconstrained Binary Optimization (QUBO) model which is presently the most
widely applied optimization model in the quantum computing area, and which
unifies a rich variety of combinatorial optimization problems. Part II (the
present paper) examines an application that augments the use of QUBO models, by
disclosing a context for coordinating QUBO solutions through a model we call
the Asset Exchange Problem (AEP). Solutions to the AEP enable individuals or
institutions to take fuller advantage of solutions to their QUBO models by
exchanges of assets that benefit all participants. Such exchanges are generated
by a combination of two optimization technologies, one grounded in network
optimization and one based on a new metaheuristic optimization approach called
combinatorial chaining. This combination provides a flexibility to solve AEP
variants that open the door to additional links to quantum computing
applications and additional applications via the Quantum Bridge Analytics
perspective. We show how this modeling and solution capability gives rise to an
Asset Exchange Technology that embraces a broad range of financial, industrial,
scientific and social settings. Examples are presented that show the nature of
these processes from a tutorial perspective.
