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A recent Google study [Phys. Rev. X, 6:031015 (2016)] compared a D-Wave 2Xquantum processing unit (QPU) to two classical Monte Carlo algorithms:simulated annealing (SA) and quantum Monte Carlo (QMC). The study showed theD-Wave 2X to be up to 100 million times faster than the classical algorithms.The Google inputs are designed to demonstrate the value of collectivemultiqubit tunneling, a resource available to D-Wave QPUs but not to simulatedannealing. But the computational hardness in these inputs is highly localizedin gadgets, with only a small amount of complexity coming from globalinteractions, meaning that the relevance to real-world problems is limited.  In this study we provide a new synthetic problem class that addresses thelimitations of the Google inputs while retaining their strengths. We use simpleclusters instead of more complex gadgets and more emphasis is placed oncreating computational hardness through frustrated global interactions likethose seen in interesting real-world inputs. The logical problems used togenerate these inputs can be solved in polynomial time [J. Phys. A, 15:10(1982)]. However, for general heuristic algorithms that are unaware of theplanted problem class, the frustration creates meaningful difficulty in acontrolled environment ideal for study.  We use these inputs to evaluate the new 2000-qubit D-Wave QPU. We include theHFS algorithm---the best performer in a broader analysis of Google inputs---andwe include state-of-the-art GPU implementations of SA and QMC. The D-Wave QPUsolidly outperforms the software solvers: when we consider pure annealing time(computation time), the D-Wave QPU reaches ground states up to 2600 timesfaster than the competition. In the task of zero-temperature Boltzmann samplingfrom challenging multimodal inputs, the D-Wave QPU holds a similar advantage asquantum sampling bias does not seem significant.

Quantum Annealing amid Local Ruggedness and Global Frustration