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