2014/12/10 by Nathan Wiebe, Ashish Kapoor, Wiebe, Nathan +3 · 2 voices · 6 citations
Computer Science · #Neural Networks and Reservoir Computing #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #cs.LG #cs.NE #quant-ph
paper · pdf · doi:10.48550/arxiv.1412.3489
openalex publication_date 2014/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.