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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

Self-supervised pre-trained neural network for quantum natural language processing.

Ben Yao¹, Prayag Tiwari², Qiuchi Li¹

¹Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.

Neural Networks : the Official Journal of the International Neural Network Society

|December 13, 2024

Summary

This summary is machine-generated.

This study enhances quantum natural language processing (NLP) by using self-supervised pre-training to improve sentence encoding. This approach boosts the representation power of quantum NLP models for better text classification performance.

Keywords:

Natural language processing Quantum computing Self-supervised pre-training

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Area of Science:

Quantum Computing
Natural Language Processing

Background:

Quantum computing models show promise but face limitations in natural language processing (NLP) due to linearity.
Current quantum NLP models have restricted representation capacity.

Purpose of the Study:

To address the representation limitations in quantum NLP models.
To enhance the power of quantum NLP models using self-supervised pre-training.

Main Methods:

Developed a self-supervised pre-training approach for quantum sentence encodings.
Fine-tuned quantum circuits for downstream NLP tasks based on pre-trained encodings.

Main Results:

Pre-trained quantum NLP models demonstrated significant improvements over purely quantum models.
Achieved meaningful prediction results on various text classification datasets.

Conclusions:

Self-supervised pre-training effectively increases the representation capacity of quantum NLP models.
This method offers a promising direction for advancing quantum natural language processing applications.