Success typically involves vast numbers of parameters and voluminous amounts of training data.
Machine-learning algorithms perform tasks such as image recognition by finding hidden structures and patterns in data, then creating mathematical models that allow the algorithm to recognize the same patterns in other data sets. Success typically involves vast numbers of parameters and voluminous amounts of training data. But with quantum versions of machine learning, the huge range of different states open to quantum particles means that the routines could require fewer parameters and much less training data.