  Noisy intermediate-scale quantum (NISQ) devices are spearheading the second
quantum revolution. Of these, quantum annealers are the only ones currently
offering real world, commercial applications on as many as 5000 qubits. The
size of problems that can be solved by quantum annealers is limited mainly by
errors caused by environmental noise and intrinsic imperfections of the
processor. We address the issue of intrinsic imperfections with a novel error
correction approach, based on machine learning methods. Our approach adjusts
the input Hamiltonian to maximize the probability of finding the solution. In
our experiments, the proposed error correction method improved the performance
of annealing by up to three orders of magnitude and enabled the solving of a
previously intractable, maximally complex problem.
