Benchmarking the D-Wave Two

Catherine C. McGeoch

Daniel O’Malley, Velimir V. Vesselinov, Boian S. Alexandrov +1 more

D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest. Much of this interest has focused on the quantum behavior of D-Wave machines, and there have been few practical algorithms that use the D-Wave. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method takes a matrix as input and produces two low-rank matrices as output-one containing latent features in the data and another matrix describing how the features can be combined to approximately reproduce the input matrix. Despite the limited number of bits in the D-Wave hardware, this method is capable of handling a large input matrix. The D-Wave only limits the rank of the two output matrices. We apply this method to learn the features from a set of facial images and compare the performance of the D-Wave to two classical tools. This method is able to learn facial features and accurately reproduce the set of facial images. The performance of the D-Wave shows some promise, but has some limitations. It outperforms the two classical codes in a benchmark when only a short amount of computational time is allowed (200-20,000 microseconds), but these results suggest heuristics that would likely outperform the D-Wave in this benchmark.

Dilina Perera, Bhalgamiya, Bhavika, M. A. Novotny

We investigate an efficient, generic method for evaluating the performance of quantum annealing devices that does not require the prior knowledge of the true ground state of the benchmark problem. This approach exploits symmetry properties inherent to the ground states of a composite Hamiltonian comprising the benchmark problem Hamiltonian and its symmetric counterpart. Using this method, we compare the performance of two generations of D-Wave machines. Although we do not observe a noticeable difference in the probability of finding solutions with the required symmetry, our results suggest that the current generation of D-Wave machines notably outperforms its predecessor in finding states closer to those with the required symmetry.

Damian S. Steiger, Bettina Heim, Troels F. Rønnow +1 more

In this paper, we provide an introduction to quantum annealers, which are analogue quantum computing devices, and their potential application to solve hard optimisation problems. We summarise our benchmarks performed on a "Wave Two" machine by Canadian company D-Wave Systems Inc.

P. Aaron Lott