Progress of Grover Quantum Search Algorithm

Luan, Linlin; Wang, Zhijie; Liu, Sanming

doi:10.1016/j.egypro.2012.01.263

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…Lov Grover in 1996 [1] invented the Grover Search algorithm to perform the fastest possible quantum search for an unsorted database which takes O(√n) time and O(logN) storage space. This mean that, for a list of 108 elements, the number of iterations required to search a desired item is order of 10000 and the obtained result will be accurate with a probability close to 1.…”

Section: Grover's Quantum Search Algorithmmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Jingle,

Sam,

Paul

et al. 2021

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In this paper, we design and analyse the Circuit for Grover's Quantum Search Algorithm on 2, 3 and 4-qubit systems, in terms of number of gates, representation of state vectors and measurement probability for the state vectors. We designed, examined and simulated the quantum circuit on IBM Q platform using Quantum Programming Studio. We present the theoretical implementation of the search algorithm on different qubit systems. We observe that our circuit design for 2 and 4-qubit systems are precise and do not introduce any error while experiencing a small error to our design of 3-qubit quantum system.

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Section: Grover's Quantum Search Algorithmmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Jingle,

Sam,

Paul

et al. 2021

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“…It is important to note that, due to the nature of quantum states, in a universal quantum computer there is a polynomial time and resource mapping from the classical gate model of these other approaches to a quantum computer. We illustrate this in Figure 8 for Shor's [44] and Grover's [45] algorithms.…”

Section: ) Universal Quantum Computingmentioning

confidence: 99%

“…On a classical machine, to perform the same function, O(n) operations are required. This advantage is exploited by Grover [45] to reduce the time spent searching an unsorted database from O(n) to O(n (1/2) . However, how BQP problems related to NP-Complete ones or whether there is a well defined or unique relationship remains an open question.…”

Section: B Complexitymentioning

confidence: 99%

Machine Learning: Quantum vs Classical

2020

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Encouraged by growing computing power and algorithmic development, machine learning technologies have became powerful tools for a wide variety of application areas, spanning from agriculture to chemistry and natural language processing. The use of quantum systems to process classical data using machine learning algorithms has given rise to an emerging research area, i.e. quantum machine learning. Despite its origins in the processing of classical data, quantum machine learning also explores the use of quantum phenomena for learning systems, the use of quantum computers for learning on quantum data and how machine learning algorithms and software can be formulated and implemented on quantum computers. Quantum machine learning can have a transformational effect in computer science. It may speed up the processing of information well beyond the existing classical speeds. Recent work has seen the development of quantum algorithms that could serve as foundations for machine learning applications. Despite its great promise, there are still significant hardware and software challenges that need to be resolved before quantum machine learning becomes practical. In this paper, we present an overview of quantum machine learning in the light of classical approaches. Departing from foundational concepts of machine learning and quantum computing, we discusses various technical contributions, strengths and similarities of research works in this domain. We also elaborate upon recent progress of different quantum machine learning approaches, their complexity and applications in various fields such as physics, chemistry and natural language processing.

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