Challenges and opportunities in quantum machine learning

At the intersection of machine learning and quantum computing, has potential accelerating data analysis, especially for data, with applications materials, biochemistry high-energy physics. Nevertheless, challenges remain regarding trainability models. Here we review current methods learning. We highlight differences between classical learning, a focus on neural networks deep Finally, discuss opportunities advantage Quantum become an essential tool to process analyze increased amount data. Despite recent progress, there are still many be addressed myriad future avenues research.