Benchmarking the D-Wave 2X: Challenges and Early Results

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 quantum 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.