Exploring The Power Of Data In Quantum Machine Learning

Quantum computers have the capability to develop quantum machine learning algorithms. These algorithms can achieve better performance for modeling quantum-mechanical systems such as molecules, catalysts, or high-temperature superconductors. Since it is difficult for classical computers to handle the interference of the exponentially evolving states in the quantum world, quantum computers are expected to have an advantage in quantum originated-machine learning problems. The quantum advantage extends to machine learning problems in the classical domain, for example, computer vision or natural language processing. Fig1:Classical computers can solve a problem better with the help of data obtained in nature (e.g., physical experiments).