We know that quantum computing is a complex subject, but the resources section has content aimed at people with differing levels of knowledge. Watch a video, read a tutorial or white paper or one of the many scientific papers that have been published about all aspects of D-Wave and quantum computing. 


Quantum computing is not a familiar topic to most people, nor is programming a quantum computer. Our tutorials provide background information for those interested in understanding quantum computers and how to program them.

How D-Wave processors are built, and how they use the physics of spin systems to implement quantum computation The aim of this document is to describe how a quantum computer is physically built, how quantum bits and their associated circuitry are created, addressed, and controlled, and what is happening inside the computer when programmers send information to a D-Wave quantum machine.


D-Wave has published more than 70 peer-reviewed papers in scientific journals including Nature, Science, Physical Review and others. There are also many other papers written by independent scientists about the D-Wave technology. You can find links to them from the publications page.

Amir Khoshaman, Walter Vinci, Brandon Denis, Evgeny Andriyash,and Mohammad H. Amin 

Variational autoencoders (VAEs) are powerful generative models with the salient ability to perform inference. Here, we introduce a quantum variational autoencoder (QVAE): a VAE whose latent generative process is implemented as a quantum Boltzmann machine (QBM). We show that our model can be trained end-to-end by maximizing a well-defined loss-function: a “quantum” lower- bound to a variational approximation of the log-likelihood. We use quantum Monte Carlo (QMC) simulations to train and evaluate the performance of QVAEs. To achieve the best performance, we first create a VAE platform with discrete latent space generated by a restricted Boltzmann machine (RBM). Our model achieves state-of-the-art performance on the MNIST dataset when compared against similar approaches that only involve discrete variables in the generative process. We consider QVAEs with a smaller number of latent units to be able to perform QMC simulations, which are computationally expensive. We show that QVAEs can be trained effectively in regimes where quantum effects are relevant despite training via the quantum bound. Our findings open the way to the use of quantum computers to train QVAEs to achieve competitive performance for generative models. Placing a QBM in the latent space of a VAE leverages the full potential of current and next-generation quantum computers as sampling devices.

(22 Feb 2018) https://arxiv.org/pdf/1802.05779.pdf