Where Will Quantum Systems Succeed in AI Training?
(NextPlatform) AI training is of special interest for quantum computing even at this early stage of of the technology. AI training is a compute-intensive task when done at scale which often requires multi-GPU laden nodes for training sets with many thousands or even millions of examples.
Quantum machine learning is a vastly differentiated field, but for future AI, one area of particular area of interest is using a quantum annealing system like the D-Wave machine for training Boltzmann machines and other neural network models. The D-Wave 2X at NASA Ames has been used to train Boltzmann machines and neural networks and some similar work has been done to generate and train datasets for handwritten characters.
University of Michigan researcher, Dr. Veera Sundararaghavan, provides indepth perspectives on the current and future states of practice. He explained that today we are qubit-limited, so in the near-term quantum-classical hybrid algorithms are critical.
Early machine learning algorithms on quantum computers will take advantage of the ability of quantum computers to sample complex probability distributions.
Real-world use-cases for discriminative tasks will involve problems where a rapid classification needs to be made, typically in military applications. Which is why the defense industry is interested in quantum computers.