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