A quantum-inspired genetic algorithm based on probabilistic coding for multiple sequence alignment.

Quantum parallelism arises from the ability of a quantum memory register to exist in a superposition of base states. Since the number of possible base states is 2(n), where n is the number of qubits in the quantum memory register, one operation on a quantum computer performs what an exponential number of operations on a classical computer performs. The power of quantum algorithms comes from taking advantages of quantum parallelism.

Repeats identification using improved suffix trees.

The suffix tree data structure plays an important role in the efficient implementations of some querying algorithms. This paper presents the fast Rep(eats)Seeker algorithm for repeats identification based on the improvements of suffix tree construction. The leaf nodes and the branch nodes are numbered in different ways during the construction of a suffix tree and extra information is added to the branch nodes.

A probabilistic coding based quantum genetic algorithm for multiple sequence alignment.

This paper presents an original Quantum Genetic algorithm for Multiple sequence ALIGNment (QGMALIGN) that combines a genetic algorithm and a quantum algorithm. A quantum probabilistic coding is designed for representing the multiple sequence alignment. A quantum rotation gate as a mutation operator is used to guide the quantum state evolution.