Quantum Information Processing Via Hamiltonian  Inverse Quantum Engineering

Quantum Information Processing Via Hamiltonian Inverse Quantum Engineering

Alan C. Santos

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Universidade Federal Fluminense

Quantum Information Processing Via Hamiltonian Inverse Quantum Engineering

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Abstract

In this paper we discuss how we can design Hamiltonians to implement quantum algorithms, in particular we focus in Deutsch and Grover algorithms. As main result of this paper, we show how Hamiltonian inverse quantum engineering method allow us to obtain feasible and time-independent Hamiltonians for implementing such algorithms. From our approach for the Deutsch algorithm, different from others techniques, we can provide an alternative approach for implementing such algorithm where no auxiliary qubit and additional resources are required. In addition, by using a single quantum evolution, the Grover algorithm can be achieved with high probability 1 -𝜖𝜖 2 , where 𝜖𝜖 is a very small arbitrary parameter.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Alan C. Santos. 2018. \u201cQuantum Information Processing Via Hamiltonian Inverse Quantum Engineering\u201d. Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 18 (GJSFR Volume 18 Issue F3).

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GJSFR Volume 18 Issue F3

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

Keywords

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Classification

GJSFR-F Classification MSC 2010: 03G12

Submission ReceivedFebruary 4, 2018
Peer Review Double Blind
Handling Editor
Accepted March 3, 2018
Published March 17, 2018

Version of record

v1.2

Issue date

April 23, 2018

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Article Matrices

Total Score: 101

Country: Brazil

Subject: Global Journal of Science Frontier Research - F: Mathematics & Decision

Authors: Alan C. Santos (PhD/Dr. count: 0)

View Count (all-time): 142

Total Views (Real + Logic): 3134

Total Downloads (simulated): 1532

Publish Date: 2018 04, Mon

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Total Views: 3134

Total Downloads: 1532

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