Quantum machine learning for chemistry and physics.

Area of Science:

• Physical Sciences, specifically Chemistry
• Materials Science
• Drug Design
• Chemical Dynamics

Background:

• Machine learning (ML) and deep learning (DL) identify patterns in data for predictive modeling.
• These AI techniques have driven significant advancements across physical sciences, particularly chemistry.
• Emerging ML algorithms are compatible with near-term quantum hardware.

Purpose of the Study:

• To review machine learning applications in chemistry.
• To highlight contributions from classical and quantum computing-enhanced ML.
• To foster interdisciplinary research and accelerate ML algorithm development in chemistry.

Main Methods:

• Explication of selected ML topics and techniques.
• Overview of classical and quantum computing-enhanced ML algorithms.
• Statistical physical insights into ML learning strategies.

Main Results:

• ML has revolutionized materials design, photovoltaics, and electronic structure calculations.
• ML aids in computing force fields, chemical reaction dynamics, and drug design.
• ML enables accurate classification of matter phases and identification of emergent criticality.

Conclusions:

• ML and DL are powerful tools transforming chemical research and discovery.
• Quantum computing integration with ML shows promising outcomes for complex problems.
• Cross-pollination of ideas between ML and chemistry will accelerate scientific progress.