Evolution of Quantum Computing Based on Grover's Search Algorithm

Author(s):  
Prakhar Shrivastava ◽  
Kapil Kumar Soni ◽  
Akhtar Rasool
2019 ◽  
Vol 19 (1&2) ◽  
pp. 35-66
Author(s):  
Yiwei Li ◽  
Edison Tsai ◽  
Marek Perkowski ◽  
Xiaoyu Song

Functional decomposition plays a key role in several areas such as system design, digital circuits, database systems, and Machine Learning. This paper presents a novel quantum computing approach based on Grover’s search algorithm for a generalized Ashenhurst-Curtis decomposition. The method models the decomposition problem as a search problem and constructs the oracle circuit based on the set-theoretic partition algebra. A hybrid quantum-based algorithm takes advantage of the quadratic speedup achieved by Grover’s search algorithm with quantum oracles for finding the minimum-cost decomposition. The method is implemented and simulated in the quantum programming language Quipper. This work constitutes the first attempt to apply quantum computing to functional decomposition.


COSMOS ◽  
2006 ◽  
Vol 02 (01) ◽  
pp. 71-80
Author(s):  
K. W. CHOO

This article reviews quantum computing and quantum algorithms. Some insights into its potential in speeding up computations are covered, with emphasis on the use of Grover's Search. In the last section, we discuss applications of quantum algorithm to bioinformatics. In particular, the extension of quantum counting algorithm to protein mass spectra counting is proposed.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Valentin Gebhart ◽  
Luca Pezzè ◽  
Augusto Smerzi

AbstractDespite intensive research, the physical origin of the speed-up offered by quantum algorithms remains mysterious. No general physical quantity, like, for instance, entanglement, can be singled out as the essential useful resource. Here we report a close connection between the trace speed and the quantum speed-up in Grover’s search algorithm implemented with pure and pseudo-pure states. For a noiseless algorithm, we find a one-to-one correspondence between the quantum speed-up and the polarization of the pseudo-pure state, which can be connected to a wide class of quantum statistical speeds. For time-dependent partial depolarization and for interrupted Grover searches, the speed-up is specifically bounded by the maximal trace speed that occurs during the algorithm operations. Our results quantify the quantum speed-up with a physical resource that is experimentally measurable and related to multipartite entanglement and quantum coherence.


Grover’s quantum search algorithm allows quadratic speedup in unsorted search problem by utilizing amplitude amplification trick in quantum computing. In this paper, an approach to implement Grover’s quantum search algorithm is proposed. The implementation is done using Rigetti Forest and Python. The testing and evaluation processes are carried on in two computers with different hardware specifications to derive more information from the result. The results are measured in user time and compared with implementation from Quantum Computing Playground. The user time of this implementation for 10 qubits and 1024 data is slower compared to Quantum Computing Playground’s implementation. The proposed implementation can be improved by calculating the probability of Grover’s quantum search algorithm in finding the appropriate search result.


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