Revealing Trends in Quantum Technology with Natural Language Processing and Deep Learning Techniques

Recent years have witnessed a surge of interests in exploring emerging research directions of quantum technology, including quantum computing, quantum sensors, quantum cryptography, etc., and the number of scientific publications in the literature in this field has been growing steadily. However, due to the large volume of unstructured text data and the lack of automatic archiving of the obtained results, much of the information remains buried in textual details and can not be fully exploited for further usage and analysis. To meet this challenge, this project explores natural language processing (NLP) and deep learning (DL) methods to automatically discover useful patterns from such publications. More specifically, the project focuses on automatically retrieving academic papers, e.g., held in arXiv, Google Scholar, etc., summarizing large scale text collections and discovering ongoing hot topics across quantum tech, and presenting the insights on evolving trends back to end users. The discovered knowledge may help quantum tech companies, investors and governments identify future directions and trends, and ultimately gain a competitive advantage.

Faculty Supervisor:

Yang Liu;Xu Sunny Wang

Student:

Partner:

The Quantum Daily Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Wilfrid Laurier University

Program: