Quantum Text Encoding for Classification Tasks
arXiv:2301.03715 · doi:10.1109/SEC54971.2022.00052
Abstract
This paper explores text classification on quantum computers. Previous results have achieved perfect accuracy on an artificial dataset of 100 short sentences, but at the unscalable cost of using a qubit for each word. This paper demonstrates that an amplitude encoded feature map combined with a quantum support vector machine can achieve 62% average accuracy predicting sentiment using a dataset of 50 actual movie reviews. This is still small, but considerably larger than previously-reported results in quantum NLP.
References in corpus (6)
- Efficient Estimation of Word Representations in Vector Space
- Supervised learning with quantum enhanced feature spaces
- Benchmarking an 11-qubit quantum computer
- A divide-and-conquer algorithm for quantum state preparation
- On the robustness of bucket brigade quantum RAM
- Quantum Self-Attention Neural Networks for Text Classification