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Springer eBooks

IBM Quantum Platform

Herbert M. Rubin

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-3-662-71207-8_19

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In order to develop and validate the quantum image processing algorithm on quantum computer, combined with quantum computing operation on IBM quantum experiment platform (IBM Q) and quantum image processing theory, a quantum image segmentation method based on IBM Q platform was designed. An improved enhanced quantum representation (IEQR) was proposed based on the existing novel enhanced quantum representation (NEQR). The quantum image segmentation circuit, which consists of the quantum bit string comparator (QBSC) and the Controlswap (Cswap) gate, is initialized according to the IEQR. Finally, quantum image segmentation of 2×2 and 4×4 size images are realized on IBM Q and local classical computer simulator. The experimental results demonstrate the feasibility and effectiveness of the algorithm and the superiority of quantum computers is validated.

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The article defines the criteria for choosing a cloud-based platform for mastering the basics of quantum informatics by students of a specialized (high) schools: cross-browser; intuitive interface; the possibility of free access; access without registration and simplified registration; the presence of a systematized reference system with examples; support for the development of the environment by the developer; support for working in a personal educational environment; support for working with quantum algorithms in graphical mode; automatic conversion of quantum algorithms from graphic format to program code text; support for the Ukrainian-language localization; availability of a mobile application; responsive design. The possibilities of platforms for implementing quantum algorithms from the following companies are analyzed: Microsoft, QuTech, Amazon Braket, IBM. The choice of the IBM Quantum cloud-based platform is justified. Work at IBM Quantum Composer and IBM Quantum Lab is described. Information about quantum operations and gates is presented: their designation in IBM Quantum Composer and IBM Quantum Lab, the gate matrix, and the purpose of the gate. An example of implementing quantum teleportation in the form of a circuit and program is given.
The article defines the criteria for choosing a cloud-oriented platform for mastering the basics of quantum informatics by students of a specialized (high) school: cross-browser; intuitive interface; the possibility of free (free) access; access without registration and simplified registration; the presence of a systematized reference system with examples; support for the development of the environment by the developer; support for working in a personal educational environment; support for working with quantum algorithms in graphical mode; automatic conversion of quantum algorithms from graphic format to program code text; support for the Ukrainian-language locale; availability of a mobile application; responsive design. The possibilities of platforms for implementing quantum algorithms from the following companies are analyzed: Microsoft, QuTech, Amazon Braket, IBM. The choice of the IBM Quantum Cloud-based platform is justified. Work at IBM Quantum Composer and IBM Quantum Lab is described. Information about quantum operations and Gates is presented: their designation in IBM Quantum Composer and IBM Quantum Lab; the gate Matrix; and the purpose of the gate. An example of implementing quantum teleportation in the form of a scheme and program is given.
In order to develop and validate the quantum image processing algorithm on quantum computer, combined with quantum computing operation on IBM quantum experiment platform (IBM Q) and quantum image processing theory, a quantum image segmentation method based on IBM Q platform was designed. An improved enhanced quantum representation (IEQR) was proposed based on the existing novel enhanced quantum representation (NEQR). The quantum image segmentation circuit, which consists of the quantum bit string comparator (QBSC) and the Controlswap (Cswap) gate, is initialized according to the IEQR. Finally, quantum image segmentation of 2×2 and 4×4 size images are realized on IBM Q and local classical computer simulator. The experimental results demonstrate the feasibility and effectiveness of the algorithm and the superiority of quantum computers is validated.