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Pattern Classifications Using Grover’s and Ventura’s Algorithms in a Two-qubits System

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Abstract

Carrying out the classification of patterns in a two-qubit system by separately using Grover’s and Ventura’s algorithms on different possible superposition, it has been shown that the exclusion superposition and the phase-invariance superposition are the most suitable search states obtained from two-pattern start-states and one-pattern start-states, respectively, for the simultaneous classifications of patterns. The higher effectiveness of Grover’s algorithm for large search states has been verified but the higher effectiveness of Ventura’s algorithm for smaller data base has been contradicted in two-qubit systems and it has been demonstrated that the unknown patterns (not present in the concerned data-base) are classified more efficiently than the known ones (present in the data-base) in both the algorithms. It has also been demonstrated that different states of Singh-Rajput MES obtained from the corresponding self-single- pattern start states are the most suitable search states for the classification of patterns |00>,|01 >, |10> and |11> respectively on the second iteration of Grover’s method or the first operation of Ventura’s algorithm.

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Acknowledgments

Authors thankfully acknowledge the financial support of University Grants Commission (UGC), New Delhi (India) in the form of a major research project: MRP-Major-Comp-2013-39460.

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Authors and Affiliations

  1. Department of Computer Science, Dr. B R. Ambedkar University, Agra, 282002, UP, India

    Manu Pratap Singh & Kishori Radhey

  2. Kumaun University, Nainital, 263 002, India

    B. S. Rajput

  3. I-11, Gamma-2, Greater Noida, India

    B. S. Rajput

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  1. Manu Pratap Singh
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  2. Kishori Radhey
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  3. B. S. Rajput
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Correspondence to B. S. Rajput.

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Singh, M., Radhey, K. & Rajput, B.S. Pattern Classifications Using Grover’s and Ventura’s Algorithms in a Two-qubits System. Int J Theor Phys 57, 692–705 (2018). https://doi.org/10.1007/s10773-017-3601-6

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  • DOI: https://doi.org/10.1007/s10773-017-3601-6

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