Dr. Kapil Kumar Soni

@nift.ac.in

Assistant Professor, Fashion Communication Department
National Institute of Fashion Technology (NIFT)

Dr. Kapil Kumar Soni


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https://researchid.co/prof.kapilsoni

EDUCATION

Ph.D. Computer Science and Engineering
M.Tech. Computer Science and Engineering
B.E. Information Technology

RESEARCH INTERESTS

Algorithms, Quantum Computing and Quantum Algorithms, Quantum Information Processing, Quantum Pattern Matching Algorithms, Quantum Machine and Deep Learning, Quantum Artificial Intelligence, Quantum Data Science, and Quantum Cryptography.
18

Scopus Publications

Scopus Publications

  • Analyzing Classical Cryptosystem Vulnerabilities Using Quantum Cryptanalysis Techniques
    Vidhi Sharma, Kapil Kumar Soni
    6th IEEE International Conference on Recent Advances in Information Technology Rait 2025, 2025
  • Quantum-effective exact multiple patterns matching algorithms for biological sequences
    Kapil Kumar Soni, Akhtar Rasool
    Peerj Computer Science, 2022
    This article presents efficient quantum solutions for exact multiple pattern matching to process the biological sequences. The classical solution takesΟ(mN) time for matching m patterns overNsized text database. The quantum search mechanism is a core for pattern matching, as this reduces time complexity and achieves computational speedup. Few quantum methods are available for multiple pattern matching, which executes search oracle for each pattern in successive iterations. Such solutions are likely acceptable because of classical equivalent quantum designs. However, these methods are constrained with the inclusion of multiplicative factor m in their complexities. An optimal quantum design is to execute multiple search oracle in parallel on the quantum processing unit with a single-core that completely removes the multiplicative factorm, however, this method is impractical to design. We have no effective quantum solutions to process multiple patterns at present. Therefore, we propose quantum algorithms using quantum processing unit withCquantum cores working on shared quantum memory. This quantum parallel design would be effective for searching alltexact occurrences of each pattern. To our knowledge, no attempts have been made to design multiple pattern matching algorithms on quantum multicore processor. Thus, some quantum remarkable exact single pattern matching algorithms are enhanced here with their equivalent versions, namely enhanced quantum memory processing based exact algorithm and enhanced quantum-based combined exact algorithm for multiple pattern matching. Our quantum solutions find alltexact occurrences of each pattern inside the biological sequence in $O((m/C)\\sqrt{N})$ and $O((m/C)\\sqrt{t})$ time complexities. This article shows the hybrid simulation of quantum algorithms to validate quantum solutions. Our theoretical–experimental results justify the significant improvements that these algorithms outperform over the existing classical solutions and are proven effective in quantum counterparts.
  • Quantum-based exact pattern matching algorithms for biological sequences
    Kapil Kumar Soni, Akhtar Rasool
    ETRI Journal, 2021
    In computational biology, desired patterns are searched in large text databases, and an exact match is preferable. Classical benchmark algorithms obtain competent solutions for pattern matching in ON time, whereas quantum algorithm design is based on Grover's method, which completes the search in ON time. This paper briefly explains existing quantum algorithms and defines their processing limitations. Our initial work overcomes existing algorithmic constraints by proposing the quantum‐based combined exact (QBCE) algorithm for the pattern‐matching problem to process exact patterns. Next, quantum random access memory (QRAM) processing is discussed, and based on it, we propose the QRAM processing‐based exact (QPBE) pattern‐matching algorithm. We show that to find all t occurrences of a pattern, the best case time complexities of the QBCE and QPBE algorithms are OtandON , and the exceptional worst case is bounded by OtandON . Thus, the proposed quantum algorithms achieve computational speedup. Our work is proved mathematically and validated with simulation, and complexity analysis demonstrates that our quantum algorithms are better than existing pattern‐matching methods.
  • Design and Analysis of Pattern Matching Algorithms Based on QuRAM Processing
    Kapil Kumar Soni, Ashwini Kumar Malviya
    Arabian Journal for Science and Engineering, 2021
  • Quantum computation based grover’s search algorithm and its variations
    Kapil Kumar Soni, Akhtar Rasool
    Recent Patents on Engineering, 2021
    Background: The future computations need parallelism, and a way to achieve the same is through quantum-based specific properties. Quantum computation supports exponential operation to be performed in parallel over a single execution step and hence achieves computational speedup. Objective: One of the quantum-based algorithms allows to search the key element over an index based unstructured database and succeeds to obtain the speedup. In the whole context of article writing, our approach reviews the significance of quantum computing, the basics required for quantum computation and their quantum logic-based circuit operations. Method: The article focuses on Grover’s search algorithm and its variations, and then illustrates the relevant amplitude amplification & phase matching techniques in accordance with the advantages and limitation to the specific perspective. Results: We made the comparative analysis between the amplitude amplification-based static & dynamic Grover’s searching and phase estimation technique. Along with this, we justified the possibilities of the empirical results and observed facts over Grover’s variant approaches. Conclusion: Finally, we conclude the analytical comparison between quantum-based searching techniques along with the applicability of Grover’s algorithm in practical computational science.
  • Quantum Based Deep Learning Models for Pattern Recognition
    Prakhar Shrivastava, Kapil Kumar Soni, Akhtar Rasool
    Communications in Computer and Information Science, 2021
  • Classical and Quantum based Differential Cryptanalysis Methods
    Priyanka Jojan, Kapil Kumar Soni, Akhtar Rasool
    2021 12th International Conference on Computing Communication and Networking Technologies Icccnt 2021, 2021
    The security of a significant proportion of cryptography in use today depends directly or indirectly on the presumed difficulty of either factoring or extracting discrete logarithms in polynomial time on quantum computers. This paper discusses a quantum version of differential cryptanalysis which propounds quadratic speedup over the existing classical one. Linear cryptanalysis and differential cryptanalysis are general form cryptanalysis which is primarily applicable to block cipher, but also to stream ciphers and cryptographic hash functions. Linear cryptanalysis is a known-plaintext attack, in which attacker studies probabilistic linear relations known as linear approximations between some bits of plaintext, some bits of cipher text and some bits of cipher key. And in differential cryptanalysis the role of attacker is to analyze how differences in input information can affect the resulting difference at the output. Differential Cryptanalysis is a chosen-plaintext attack. However, differential cryptanalysis is more compelling so in this paper we are proposing a quantum version of the same. The computational complexity of classical differential cryptanalysis reaches O(NK). But by applying variations of Grover's search that is either full or partial Quantum Search the number of queries required to find the cipher key will be reduced.
  • Quantum Computation-Based Secured Key Distribution Protocols
    Priyanka Jojan, Kapil Kumar Soni, Akhtar Rasool
    Lecture Notes in Networks and Systems, 2021
  • Quantum based Support Vector Machine Identical to Classical Model
    Disha Uke, Kapil Kumar Soni, Akhtar Rasool
    2020 11th International Conference on Computing Communication and Networking Technologies Icccnt 2020, 2020
    The paper presents an influence of machine learning techniques that can be efficiently solved using quantum computer. Machine learning is considered as a core concept of modeling and mining large dimensionality data and visualizes data instances as a vector. Support vector is one of the trending methods that use supervised learning for classifying the data efficiently irrespective of linearity or non-linearity in data. As the quantum computations already proved to be inherently parallel and can become one of the key factors in order to reduce the complexities while dealing with the classical machine learning model. The quantum machine learning takes advantage of parallelism and can explore the number of vectors and their dimensions in logarithmic time, and hence it can achieve the computational speedup. Now, the main contribution of the paper is to discuss quantum fundamental, quantum algorithm and then to review over classical equivalent quantum support vector machine along with their training algorithm and the comparative analysis between algorithmic growths. At last, the paper justifies the quantum support vector as a highly efficient model that gains the computational speedup and also shows the possibility of further improvement.
  • Classical Equivalent Quantum Based Efficient Data Preprocessing Algorithm
    Barkha Soni, Nilay Khare, Kapil Kumar Soni, Akhtar Rasool
    2020 11th International Conference on Computing Communication and Networking Technologies Icccnt 2020, 2020
    The machine learning model can infer desired information on processing data sets without being explicitly programmed. It needs refined data to train the perfect model, hence preprocessing is mandatory. The principal component analysis is desired classical existing methods for preprocessing and it requires polynomial time. Now, the research field of computer science is getting influenced by existence of quantum computations, as it supports exponential operations to be performed in parallel over single step of execution. An intrinsic realization of quantum machine provides simultaneous access to either classical or quantum memory. The objective of paper is to understand the importance of data preprocessing and to suggest quantum based solution that takes the advantage of quantum parallelism and thus can obtain computational speedups. So, we contribute to the emergence of quantum computations, processing aspects using quantum accessible memory models and then classical equivalent quantum principal component analysis algorithm. At last we conclude with the mathematical justification over complexity analysis, computational speedups, and prove that quantum algorithms are efficient along with the suggestions of further directions.
  • Pattern Matching: A Quantum Oriented Approach
    Kapil Kumar Soni, Akhtar Rasool
    Procedia Computer Science, 2020
  • Classical Equivalent Quantum Unsupervised Learning Algorithms
    Prakhar Shrivastava, Kapil Kumar Soni, Akhtar Rasool
    Procedia Computer Science, 2020
  • Classical Equivalent Quantum Based Pattern Matching Algorithms
    Kapil Kumar Soni, Akhtar Rasool
    2019 10th International Conference on Computing Communication and Networking Technologies Icccnt 2019, 2019
  • Evolution of Quantum Computing Based on Grover's Search Algorithm
    Prakhar Shrivastava, Kapil Kumar Soni, Akhtar Rasool
    2019 10th International Conference on Computing Communication and Networking Technologies Icccnt 2019, 2019
  • An introduction to quantum search algorithm and its implementation
    Jose P. Dumas, Kapil Soni, Akhtar Rasool
    Advances in Intelligent Systems and Computing, 2019
  • Cryptographic attack possibilities over RSA algorithm through classical and quantum computation
    Kapil Kumar Soni, Akhtar Rasool
    Proceedings of the International Conference on Smart Systems and Inventive Technology Icssit 2018, 2018
  • Searching speedup through quantum computing
    Journal of Advanced Research in Dynamical and Control Systems, 2018
  • Word Size Removal (WSR) method for Bit Parallel String Matching
    Kapil Kumar Soni, Vivek Sharma
    Souvenir of the 2015 IEEE International Advance Computing Conference Iacc 2015, 2015