Researchers developed practical zero-noise extrapolation techniques for quantum annealing that successfully mitigate both thermal and non-thermal errors in quantum systems without additional qubit overhead, demonstrated through experiments on a transverse-field Ising spin chain that aligned well with theoretical predictions.
D-Wave Systems has published a milestone study in collaboration with scientists at Google, demonstrating a computational performance advantage, increasing with both simulation size and problem hardness, to over 3 million times that of corresponding classical methods.
Boltzmann machines are the basis of several deep learning methods that have been successfully applied to both supervised and unsupervised machine learning tasks. These models assume that a dataset is generated according to a Boltzmann distribution, and the goal of the training procedure is to learn the set of parameters that most closely match the input data distribution.