A D-Wave platform implements a *quantum annealing* algorithm in hardware, to solve an NP-hard problem known as Ising Model Optimization (also called Quadratic Unconstrained Boolean Optimization). The ``hardware'' is a processor chip containing qubits that exploit quantum properties such as superposition and entanglement to carry out the computation. This is a heuristic algorithm that belongs to the *adiabatic quantum model* of computation, an alternative to the more familiar *q**uantum gate model *of computation.

The task of performance assessment for these novel platforms -- comparing classical heuristics implemented in software to a quantum analog heuristic implemented in hardware -- gives rise to a number of new methodological issues, on top of the usual challenges relating to evaluation of heuristics for NP-hard problems. I will discuss some of these issues and present some early performance results for the D-Wave 2X, a 1000-qubit processor launched in summer 2015.