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Abstract. We present several results on comparative complexity for different variants of OBDD models.-We present some results on comparative complexity of classical and quantum OBDDs. We consider a partial function depending on parameter k such that for any k > 0 this function is computed by an exact quantum OBDD of width 2 but any classical OBDD (deterministic or stable bounded error probabilistic) needs width 2 k+1 . -We consider quantum and classical nondeterminism. We show that quantum nondeterminism can be more efficient than classical one. In particular, an explicit function is presented which is computed by a quantum nondeterministic OBDD with constant width but any classical nondeterministic OBDD for this function needs non-constant width. -We also present new hierarchies on widths of deterministic and nondeterministic OBDDs. We focus both on small and large widths.

We present a version of quantum hash functions based on non-binary discrete functions. The proposed quantum procedure is 'classical-quantum', that is, it takes a classical bit string as an input and produces a quantum state. The resulting function has the property of a one-way function (pre-image resistance); in addition it has properties analogous to classical cryptographic hash second pre-image resistance and collision resistance.We also show that the proposed function can be naturally used in a quantum digital signature protocol.

This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presents the fundamentals of qubits, quantum registers, and quantum states, introduces important quantum tools based on known quantum search algorithms and SWAP-test, and discusses the basic quantum procedures used for quantum search methods. The second part, "quantum classification algorithms", introduces several classification problems that can be accelerated by using quantum subroutines and discusses the quantum methods used for classification.