Quantum Deep Learning
This addresses the challenge of computational inefficiency in deep learning for researchers and practitioners, offering a novel quantum approach that could enhance model training.
The paper tackles the problem of training deep learning models more efficiently by using quantum computing, showing that it reduces training time for restricted Boltzmann machines and improves optimization, with methods enabling efficient training of full Boltzmann machines and multi-layer models.
In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of full Boltzmann machines and multi-layer, fully connected models and do not have well known classical counterparts.