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Journal of Global Research in Computer Sciences

A SIMPLE EXPLANATION OF SEARCH TECHNIQUE IN QUANTUM FRAMEWORK

Shaktikanta Nayak, Sitakanta Nayak, Jaskaran Singh

Published Nov 12, 2012• 1 citation
OpenAlex

Abstract

The quantum search takes advantage of quantum parallelism to construct superposition of all possible states and then increase the probability amplitude of the solution state. This is the distinguishing characteristics of quantum search strategy. The objective of a classical search algorithm is to diminish the amplitude of target state where as a quantum search algorithm tries to amplify the amplitude of the target state. The term amplification indicates to increase the probability of the target state. This paper attempts to explain two central ideas of Grover’s quantum search algorithm that amplify the probability of the target state, the inversion about the mean and phase inversion, in a simplified way with a concrete example.

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The quantum search takes advantage of quantum parallelism to construct superposition of all possible states and then increase the probability amplitude of the solution state. This is the distinguishing characteristics of quantum search strategy. The objective of a classical search algorithm is to diminish the amplitude of target state where as a quantum search algorithm tries to amplify the amplitude of the target state. The term amplification indicates to increase the probability of the target state. This paper attempts to explain two central ideas of Grover’s quantum search algorithm that amplify the probability of the target state, the inversion about the mean and phase inversion, in a simplified way with a concrete example.
Quantum search/amplitude amplification algorithms are designed to be able to amplify the amplitude in the target state linearly with the number of operations. Since the probability is the square of the amplitude, this results in the success probability rising quadratically with the number of operations. This paper presents a new kind of quantum search algorithm in which the amplitude of the target state, itself increases quadratically with the number of operations. However, the domain of applications of this is much more limited than standard amplitude amplification.
Abstract—Quantum mechanical computers unlike classical computers can be in a superposition of states and carry out multiple operations at the same time. Grover has shown that a quantum computer can do a random database search in O ( √ N) steps. He has further proved that using the superposition of the states in quantum computing, a quantum computer should be able to do a search in a single step. He has stated that the query generated to do this search will require Ω(NlogN) steps. In this paper we aim at optimizing the Grover’s search algorithm. In our algorithm, we have repeated the inversion step a number of times instead of stopping after a single step. Measurement after a single step required a larger number of subsystems which impaired the effectiveness of the algorithm leading to Ω(NlogN) steps in the preparation and processing of the query. Repetition of the inversion step brought the the number of subsystems down drastically by increasing the amplitude of the desired state.We have proved that by doing so the overall effectiveness of Grover’s algorithm can be brought down to Ω ( 3 √ N). Keywords: Quantum mechanical computers, Grover’s search algorithm, inversion step, probability and amplitude.
One iterative in Grover's original quantum search algorithm consists of two Hadamard–Walsh transformations, a selective amplitude inversion and a diffusion amplitude inversion. We concentrate on the relation among the probability of success of the algorithm, the phase shifts, the number of target items and the number of iterations via replacing the two amplitude inversions by phase shifts of an arbitrary ϕ = ϕ(0 ⩽ ϕ, ϕ ⩽ 2π). Then, according to the relation we find out the optimal phase shifts when the number of iterations is given. We present a new quantum search algorithm based on the optimal phase shifts of 1.018 after 0.5π/ iterations. The new algorithm can obtain either a single target item or multiple target items in the search space with the probability of success at least 93.43%.
Grover's algorithm, which was discovered in 1996, is faster than any classical algorithm can ever be. Quantum systems are in a superposition of states and can examine multiple data simultaneously. This work conducted a comprehensive analysis to understand how Grover's algorithm works. Grover's search provides quadratic speed in comparison to the classical one; it explores target data in the unstructured database. Grover search starts with the superposition of all Qubits, which results in the same amplitude for all data points. To achieve superposition, the Hadamard gate was applied to all Qubits. Further, a black box function named Oracle is applied to mark the desired state. In the next phase, amplitude amplification is done, which enhances the probability of the desired data point. Thus, Grover's search yields results with an O (√ N) complexity, while its counterpart classical algorithm searches the desired data point with an O (N) complexity. Experiments have been performed on the Qiskit simulator using 2 and 3 qubits for the detailed analysis of Grover's search.
We generalize Grover's unstructured quantum search algorithm to enable it to work with arbitrary starting superpositions and arbitrary unitary operators. We show that the generalized quantum search algorithm, when cast in a special orthonormal basis, can be understood as performing an exact rotation of a starting superposition into a target superposition. We derive a formula for the success probability of the generalized quantum search algorithm after n rounds of amplitude amplification. We then use this formula to determine the optimal strategy for a punctuated quantum search algorithm, i.e., one in which the amplitude amplified state is observed before the point of maximum success probability. On average, the optimal strategy is about 12% better than the naive use of Grover's algorithm. The speedup obtained is not dramatic but it illustrates that a hybrid use of quantum computing and classical computing techniques can yield a performance that is better than either alone. In addition, we show that a punctuated quantum algorithm that takes the same average computation time as Grover's standard algorithm only requires half the coherence time. We then extend the analysis to the case of a society of k quantum searches acting in parallel. We derive an analytic formula that connects the degree of parallelism with the expected computation time for k-parallel quantum search. The resulting parallel speedup scales as O(k),O(\sqrt{k}),O(k​), while the minimum number of agents needed to ensure success, k, decreases as the inverse of the square of the achievable coherence time. This result has practical significance for the design of rudimentary quantum computers that are likely to have a limited coherence time.
Background: The future computations need parallelism, and a way to achieve the same is through quantum-based specific properties. Quantum computation supports exponential operation to be performed in parallel over a single execution step and hence achieves computational speedup. Objective: One of the quantum-based algorithms allows to search the key element over an index based unstructured database and succeeds to obtain the speedup. In the whole context of article writing, our approach reviews the significance of quantum computing, the basics required for quantum computation and their quantum logic-based circuit operations. Method: The article focuses on Grover’s search algorithm and its variations, and then illustrates the relevant amplitude amplification & phase matching techniques in accordance with the advantages and limitation to the specific perspective. Results: We made the comparative analysis between the amplitude amplification-based static & dynamic Grover’s searching and phase estimation technique. Along with this, we justified the possibilities of the empirical results and observed facts over Grover’s variant approaches. Conclusion: Finally, we conclude the analytical comparison between quantum-based searching techniques along with the applicability of Grover’s algorithm in practical computational science.