Summary

In this chapter, you have learned about the adiabatic quantum computing model, which is equivalent to the quantum circuit model that we had already studied. Instead of discrete quantum gates, adiabatic quantum computing uses continuous evolution through a time-dependent Hamiltonian. You have learned how to select this Hamiltonian to encode combinatorial optimization problems and how, if the evolution is slow enough, the adiabatic theorem guarantees that we will measure the ground state at the end of the process.

You have also learned that, in practice, quantum annealing is used instead of adiabatic quantum computing, because adiabatic evolution can take too long for the process to be feasible. What is more, you now know how to use actual quantum annealers through D-Wave Leap to find approximate solutions to combinatorial optimization problems in several different ways.

You also know how to control several parameters of the annealing process, in order to improve the quality of...