The aim is to investigate how quantum machine learning can enhance regression models for materials design.
Utilized quantum computing techniques to develop regression models.
Examined the synergy between artificial intelligence and quantum machine learning.
Implemented active learning strategies in the model development process.
Quantum active learning demonstrated a significant improvement in model accuracy.
Efficient materials discovery processes were suggested through enhanced predictions.
The integration of AI with quantum computing showed promising results for future applications.
Abstract
The meeting of artificial intelligence (AI) and quantum computing is already a reality; quantum machine learning (QML) promises the design of better regression models. In this work, we extend our...