  Deep learning natural language processing models often use vector word
embeddings, such as word2vec or GloVe, to represent words. A discrete sequence
of words can be much more easily integrated with downstream neural layers if it
is represented as a sequence of continuous vectors. Also, semantic
relationships between words, learned from a text corpus, can be encoded in the
relative configurations of the embedding vectors. However, storing and
accessing embedding vectors for all words in a dictionary requires large amount
of space, and may stain systems with limited GPU memory. Here, we used
approaches inspired by quantum computing to propose two related methods, {\em
word2ket} and {\em word2ketXS}, for storing word embedding matrix during
training and inference in a highly efficient way. Our approach achieves a
hundred-fold or more reduction in the space required to store the embeddings
with almost no relative drop in accuracy in practical natural language
processing tasks.
