[3] D-Wave's early customers include Lockheed Martin, the University of Southern California, Google/NASA, and Los Alamos National Laboratory.

[5] Farris taught a business course at the University of British Columbia (UBC), where Rose obtained his PhD, and Zagoskin was a postdoctoral fellow.

The prototype was a 16-qubit quantum annealing processor, demonstrated on February 13, 2007, at the Computer History Museum in Mountain View, California.

It contains Qbsolv,[22][23][24] which is open-source software that solves quadratic unconstrained binary optimization problems on both the company's quantum processors and classic hardware architectures.

The underlying ideas for the D-Wave approach arose from experimental results in condensed matter physics, and particular work on quantum annealing in magnets performed by Gabriel Aeppli, Thomas Felix Rosenbaum, and collaborators,[31] who had been checking[32][33] the advantages,[34] proposed by Bikas K. Chakrabarti & collaborators, of quantum tunneling/fluctuations in the search for ground state(s) in spin glasses.

[37] On February 13, 2007, D-Wave demonstrated the Orion system, running three different applications at the Computer History Museum in Mountain View, California.

[citation needed] The first application, an example of pattern matching, performed a search for a similar compound to a known drug within a database of molecules.

The system is designed to solve a particular NP-complete problem related to the two-dimensional Ising model in a magnetic field.

[41] On December 8, 2009, at the Neural Information Processing Systems (NeurIPS) conference, a Google research team led by Hartmut Neven used D-Wave's processor to train a binary image classifier.

[3] A research team led by Matthias Troyer and Daniel Lidar found that, while there is evidence of quantum annealing in D-Wave One, they saw no speed increase compared to classical computers.

[47] In August 2012, a team of Harvard University researchers presented results of the largest protein-folding problem solved to date using a quantum computer.

[51] In May 2013, Catherine McGeoch, a consultant for D-Wave, published the first comparison of the technology against regular top-end desktop computers running an optimization algorithm.

[55] Unlike previous reports, this one explicitly stated that the question of quantum speedup was not something they were trying to address, and focused on constant-factor performance gains over classical hardware.

For general-purpose problems, a speedup of 15x was reported, but it is worth noting that these classical algorithms benefit efficiently from parallelization—so that the computer would be performing on par with, perhaps, 30 traditional high-end single-threaded cores.