  In this article, we show how to map a sampling of the hardest artificial
intelligence problems in space exploration onto equivalent Ising models that
then can be attacked using quantum annealing implemented in D-Wave machine. We
overview the existing results as well as propose new Ising model
implementations for quantum annealing. We review supervised and unsupervised
learning algorithms for classification and clustering with applications to
feature identification and anomaly detection. We introduce algorithms for data
fusion and image matching for remote sensing applications. We overview planning
problems for space exploration mission applications and algorithms for
diagnostics and recovery with applications to deep space missions. We describe
combinatorial optimization algorithms for task assignment in the context of
autonomous unmanned exploration. Finally, we discuss the ways to circumvent the
limitation of the Ising mapping using a "blackbox" approach based on ideas from
probabilistic computing. In this article we describe the architecture of the
D-Wave One machine and report its benchmarks. Results on random ensemble of
problems in the range of up to 96 qubits show improved scaling for median core
quantum annealing time compared with classical algorithms; whether this scaling
persists for larger problem sizes is an open question. We also review previous
results of D-Wave One benchmarking studies for solving binary classification
problems with a quantum boosting algorithm which is shown to outperform
AdaBoost. We review quantum algorithms for structured learning for multi-label
classification and introduce a hybrid classical/quantum approach for learning
the weights. Results of D-Wave One benchmarking studies for learning structured
labels on four different data sets show a better performance compared with an
independent Support Vector Machine approach with linear kernel.
