Challenges and Opportunities in Quantum Machine Learning
This is an incremental review paper that discusses opportunities and challenges in QML for researchers in quantum computing and machine learning.
The paper reviews Quantum Machine Learning (QML), addressing challenges in trainability and exploring its potential to accelerate data analysis for quantum data in fields like quantum materials and biochemistry, but does not report specific experimental results or numbers.
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics. Nevertheless, challenges remain regarding the trainability of QML models. Here we review current methods and applications for QML. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with QML.