# word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

arXiv: https://arxiv.org/abs/1911.04975 · HTML: https://mlsanity.com/paper/1911.04975/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [Exploring Extreme Parameter Compression for Pre-trained Language Models](https://mlsanity.com/paper/2205.10036/) (2022)
- [Near-Term Advances in Quantum Natural Language Processing](https://mlsanity.com/paper/2206.02171/) (2022)
- [Quantum Natural Language Processing](https://mlsanity.com/paper/2403.19758/) (2024)
- [MorphTE: Injecting Morphology in Tensorized Embeddings](https://mlsanity.com/paper/2210.15379/) (2022)
- [A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity](https://mlsanity.com/paper/2402.15333/) (2024)
- [Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding](https://mlsanity.com/paper/2508.19394/) (2025)
- [Practical Hybrid Quantum Language Models with Observable Readout on Real Hardware](https://mlsanity.com/paper/2512.12710/) (2025)
- [Quantum Semi-Supervised Kernel Learning](https://mlsanity.com/paper/2204.10700/) (2022)
- [QNet: A Quantum-native Sequence Encoder Architecture](https://mlsanity.com/paper/2210.17262/) (2022)
- [Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models](https://mlsanity.com/paper/2311.11012/) (2023)
- [Traitement quantique des langues : 'etat de l'art](https://mlsanity.com/paper/2406.15370/) (2024)
- [Quantum-machine-assisted Drug Discovery](https://mlsanity.com/paper/2408.13479/) (2024)
- [SEE: Sememe Entanglement Encoding for Transformer-bases Models Compression](https://mlsanity.com/paper/2412.12204/) (2024)
- [QCSE: A Pretrained Quantum Context-Sensitive Word Embedding for Natural Language Processing](https://mlsanity.com/paper/2509.05729/) (2025)

## Abstract (arXiv)

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.