scholarly journals Arbitrary Phase Rotation of the Marked State Cannot Be Used for Grover's Quantum Search Algorithm

1999 ◽  
Vol 32 (3) ◽  
pp. 335-338 ◽  
Author(s):  
Long GuiLu ◽  
Zhang WeiLin ◽  
Li YanSong ◽  
Niu Li
Keyword(s):  
Search Algorithm ◽  
Quantum Search ◽  
Quantum Search Algorithm ◽  
Phase Rotation ◽  
Arbitrary Phase ◽  
Marked State

scholarly journals Robust Quantum Search with Uncertain Number of Target States

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1649
Author(s):  
Yuanye Zhu ◽  
Zeguo Wang ◽  
Bao Yan ◽  
Shijie Wei
Keyword(s):  
Success Rate ◽  
Search Algorithm ◽  
Quantum Algorithms ◽  
Query Complexity ◽  
Quantum Search ◽  
Success Rates ◽  
Quantum Search Algorithm ◽  
Speed Up ◽  
Target States ◽  
Marked State

The quantum search algorithm is one of the milestones of quantum algorithms. Compared with classical algorithms, it shows quadratic speed-up when searching marked states in an unsorted database. However, the success rates of quantum search algorithms are sensitive to the number of marked states. In this paper, we study the relation between the success rate and the number of iterations in a quantum search algorithm of given λ=M/N, where M is the number of marked state and N is the number of items in the dataset. We develop a robust quantum search algorithm based on Grover–Long algorithm with some uncertainty in the number of marked states. The proposed algorithm has the same query complexity ON as the Grover’s algorithm, and shows high tolerance of the uncertainty in the ratio M/N. In particular, for a database with an uncertainty in the ratio M±MN, our algorithm will find the target states with a success rate no less than 96%.

A quantum search algorithm for future spacecraft attitude determination

2011 ◽  
Vol 68 (7-8) ◽  
pp. 1208-1218 ◽  
Author(s):  
Jack Tsai ◽  
Fu-Yuen Hsiao ◽  
Yi-Ju Li ◽  
Jen-Fu Shen
Keyword(s):  
Attitude Determination ◽  
Search Algorithm ◽  
Spacecraft Attitude ◽  
Quantum Search ◽  
Quantum Search Algorithm

How significant are the known collision and element distinctness quantum algorithms?

2004 ◽  
Vol 4 (3) ◽  
pp. 201-206
Author(s):  
L. Grover ◽  
T. Rudolph
Keyword(s):  
Search Algorithm ◽  
Quantum Algorithms ◽  
Search Space ◽  
Space Complexity ◽  
Quantum Search ◽  
Quantum Search Algorithm ◽  
Time Space ◽  
Simple Quantum ◽  
Structured Problems ◽  
Better Than

Quantum search is a technique for searching $N$ possibilities for a desired target in $O(\sqrt{N})$ steps. It has been applied in the design of quantum algorithms for several structured problems. Many of these algorithms require significant amount of quantum hardware. In this paper we propose the criterion that an algorithm which requires $O(S)$ hardware should be considered significant if it produces a speedup of better than $O\left(\sqrt{S}\right)$ over a simple quantum search algorithm. This is because a speedup of $O\left(\sqrt{S}\right)$ can be trivially obtained by dividing the search space into $S$ separate parts and handing the problem to $S$ independent processors that do a quantum search (in this paper we drop all logarithmic factors when discussing time/space complexity). Known algorithms for collision and element distinctness exactly saturate the criterion.

scholarly journals Implementation of Grover’s Quantum Search Algorithm using Rigetti Forest

2019 ◽  
Vol 8 (6S3) ◽  
pp. 110-114
Keyword(s):  
Quantum Computing ◽  
Search Algorithm ◽  
Search Problem ◽  
Quantum Search ◽  
Quantum Search Algorithm ◽  
Search Result ◽  
Amplitude Amplification ◽  
Testing And Evaluation

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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