The field of machine learning on quantum computers got a boost from new research removing a potential roadblock to the practical implementation of quantum neural networks. While theorists had previously believed an exponentially large training set would be required to train a quantum neural network, the quantum No-Free-Lunch theorem developed by Los Alamos National Laboratory shows that quantum entanglement eliminates this exponential overhead. A direct consequence of this theorem that showcases the power of data in classical machine learning is that the more data one has, the better the average performance.
Source: https://phys.org/news/2022-02-entanglement-scaling-quantum-machine.html
