Wave Quantum

Wave quantum computing research currently focuses on leveraging quantum annealers, like the D-Wave system, to accelerate machine learning tasks. This involves formulating problems such as linear regression, stereo matching, and training of neural networks (including restricted Boltzmann machines and multilayer perceptrons) as quadratic unconstrained binary optimization (QUBO) problems solvable by quantum annealing. These hybrid quantum-classical approaches aim to improve solution quality and speed compared to purely classical methods, particularly for large datasets and complex optimization problems. The ultimate goal is to demonstrate a practical advantage of quantum computing for computationally intensive machine learning applications.