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Here we develop two quantum-computational models for supervised andunsupervised classification tasks in quantum world. Presuming that the statesof a set of given quantum systems (or objects) belong to one of two knownclasses, the objective here is to decide to which of these classes each systembelongs -- without knowing its state. The supervised binary classificationalgorithm is based on having a training sample of quantum systems whose classmemberships are already known. The unsupervised binary classificationalgorithm, however, uses a quantum oracle which knows the class memberships ofthe states of the computational basis. Both algorithms require the ability toevaluate the fidelity between states of the quantum systems with unknownstates, for which here we also develop a general scheme.

Fidelity-based supervised and unsupervised learning for binary classification of quantum states