  Drawing independent samples from high-dimensional probability distributions
represents the major computational bottleneck for modern algorithms, including
powerful machine learning frameworks such as deep learning. The quest for
discovering larger families of distributions for which sampling can be
efficiently realized has inspired an exploration beyond established computing
methods and turning to novel physical devices that leverage the principles of
quantum computation. Quantum annealing embodies a promising computational
paradigm that is intimately related to the complexity of energy landscapes in
Gibbs distributions, which relate the probabilities of system states to the
energies of these states. Here, we study the sampling properties of physical
realizations of quantum annealers which are implemented through programmable
lattices of superconducting flux qubits. Comprehensive statistical analysis of
the data produced by these quantum machines shows that quantum annealers behave
as samplers that generate independent configurations from low-temperature noisy
Gibbs distributions. We show that the structure of the output distribution
probes the intrinsic physical properties of the quantum device such as
effective temperature of individual qubits and magnitude of local qubit noise,
which result in a non-linear response function and spurious interactions that
are absent in the hardware implementation. We anticipate that our methodology
will find widespread use in characterization of future generations of quantum
annealers and other emerging analog computing devices.
