Programming quantum annealing computers using machine learning

Abstract

Commercial quantum annealing (QA) machines are now being built with hundreds of quantum bits (qubits). These are used as analog computers, to solve optimization problems by annealing to an unknown ground state (the solution), given the Hamiltonian for that problem. We propose and develop a new approach, in which we use machine learning to do the inverse problem: to find the Hamiltonian that will produce a given, desired ground state. We demonstrate successful learning to produce a desired fully entangled state for a two-qubit system, then bootstrap to do the same for three, four, five and six qubits; the amount of additional learning necessary decreases. With these new capabilities the computing possibilities for QA arrays are greatly expanded.