2008•International Journal of Quantum InformationRequires access

THE CNOT QUANTUM LOGIC GATE USING q-DEFORMED OSCILLATORS

Debashis Gangopadhyay

Open publisher page 7 citations

Abstract

It is shown that the two qubit CNOT (controlled NOT) gate can also be realized using q-deformed angular momentum states constructed via the Jordan–Schwinger mechanism. Thus, all the three gates necessary for universality i.e. Hadamard, Phase Shift and the two qubit CNOT gate are realizable with q-deformed oscillators.

About this research paper

What this paper is about

It is shown that the two qubit CNOT (controlled NOT) gate can also be realized using q-deformed angular momentum states constructed via the Jordan–Schwinger mechanism. Thus, all the three gates necessary for universality i.e. Hadamard, Phase Shift and the two qubit CNOT gate are realizable with q-deformed oscillators.

Why it matters

OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

It is shown that the two qubit CNOT (controlled NOT) gate can also be realized using q-deformed angular momentum states constructed via the Jordan–Schwinger mechanism. Thus, all the three gates necessary for universality i.e. Hadamard, Phase Shift and the two qubit CNOT gate are realizable with q-deformed oscillators.

Key concepts: Controlled NOT gate, Physics, Quantum gate, Quantum mechanics, Qubit, Hadamard transform, Quantum circuit, Quantum

Related papers

Back to paper searchBrowse research topicsOriginal source
THE CNOT QUANTUM LOGIC GATE USING q-DEFORMED OSCILLATORS — Research Paper | ScholarLens