the cuckoo search algorithm is a recently developedmeta-heuristic optimization algorithm, which is suitable forsolving optimization problems. to enhance the accuracy andconvergence rate of this algorithm, an improved cuckoo searchalgorithm is proposed in this paper. normally, the parametersof the cuckoo search are kept constant. this may lead todecreasing the efficiency of the algorithm. to cop...

The study of quantum computation has been motivated by the hope of finding efficient quantum algorithms for solving classically hard problems. In this context, quantum algorithms by local adiabatic evolution have been shown to solve an unstructured search problem with a quadratic speedup over a classical search, just as Grover’s algorithm. In this paper, we study how the structure of the search...

Boaz Tamir Department of Philosophy of Science, Bar-Ilan University, Ramat-Gan, Israel. Abstract In a fundamental paper [Phys. Rev. Lett. 78, 325 (1997)] Grover showed how a quantum computer can find a single marked object in a database of size N by using only O( √ N) queries of the oracle that identifies the object. His result was generalized to the case of finding one object in a subset of ma...

We study the quantum search algorithm as an open system. More specifically, we analyze the responses of that algorithm to an external monochromatic field, and to the decoherences introduced by the measurement process. We find that the search algorithm is robust with respect to many external interactions.

A misunderstanding that an arbitrary phase rotation of the marked state together with the inversion about average operation in Grover’s search algorithm can be used to construct a (less efficient) quantum search algorithm is cleared. The π rotation of the phase of the marked state is not only the choice for efficiency, but also vital in Grover’s quantum search algorithm. The results also show t...

This paper presents a new hybrid algorithm called QDEPSO (Quantum inspired Differential Evolution with Particle Swarm Optimization) which combines differential evolution (DE), particle swarm optimization method (PSO) and quantum-inspired evolutionary algorithm (QEA) in order to solve the 0-1 optimization problems. In the initialization phase, the QDEPSO uses the concepts of quantum computing as...

We generalize Grover’s unstructured quantum search algorithm to enable it to use an arbitrary starting superposition and an arbitrary unitary matrix simultaneously. We derive an exact formula for the probability of the generalized Grover’s algorithm succeeding after n iterations. We show that the fully generalized formula reduces to the special cases considered by previous authors. We then use ...

Due to the difficulty of constructing new quantum algorithms, frameworks that facilitate this construction are of great importance in quantum computing. These frameworks reduce the problem of coming up with a quantum algorithm to that of constructing some combinatorial object that is often much simpler to reason about. The implementation and analysis of an algorithm for the specified problem fo...

It is often said that the strength of quantum computation lies in the phenomena of quantum superposition and quantum entanglement. These features of quantum computation allow performing the computation on all possible inputs that fit the quantum register. One of the greatest achievements in the theory of quantum algorithms is the quantum search algorithm introduced by Grover. A detailed descrip...

The quantum search algorithm consists of an alternating sequence of selective inversions and diffusion type operations, as a result of which it can find a target state in an unsorted database of size N in only √ N queries. This paper shows that by replacing the selective inversions by selective phase shifts of π3 , the algorithm gets transformed into something similar to a classical search algo...