Dr. Kapil Kumar Soni
@nift.ac.in/shillong
Assistant Professor, Fashion Communication Department
National Institute of Fashion Technology (NIFT)
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.
Scopus Publications
Scopus Publications
Kapil Kumar Soni and Akhtar Rasool
PeerJ
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.
Kapil Kumar Soni and Akhtar Rasool
Wiley
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.
Kapil Kumar Soni and Ashwini Kumar Malviya
Springer Science and Business Media LLC
Priyanka Jojan, Kapil Kumar Soni, and Akhtar Rasool
IEEE
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.
Prakhar Shrivastava, Kapil Kumar Soni, and Akhtar Rasool
Springer International Publishing
Kapil Kumar Soni and Akhtar Rasool
Bentham Science Publishers Ltd.
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.
Priyanka Jojan, Kapil Kumar Soni, and Akhtar Rasool
Springer Singapore
Disha Uke, Kapil Kumar Soni, and Akhtar Rasool
IEEE
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.
Barkha Soni, Nilay Khare, Kapil Kumar Soni, and Akhtar Rasool
IEEE
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.
Kapil Kumar Soni and Akhtar Rasool
Elsevier BV
Prakhar Shrivastava, Kapil Kumar Soni, and Akhtar Rasool
Elsevier BV
Prakhar Shrivastava, Kapil Kumar Soni, and Akhtar Rasool
IEEE
Quantum computing introduces an efficient way for unfolding complex problems on computing systems. It uses concept of superposition and entanglement, & hence supports intrinsic parallelism for obtaining the speedup in context of time over classical computing. Quantum algorithms can be efficiently implemented over quantum computing system. Most popular quantum algorithms like Grover's and Shor's provide a great foundation to specifically design the algorithms. These algorithms work on improving the lower runtime bounds of classical algorithms. Grover's algorithm exhibits a quadratic polynomial speedup while searching an element among ‘N’ elements unstructured database. The algorithmic strategy can be applied over various computer science related applications and some important are being considered in this article i.e. pattern matching, data mining and machine learning. This article explores quantum fundamentals; history based evolution followed by Grover's search algorithm in detail with cascading advantages, limitations & applications and also finally concludes with the significance of quantum computing & quantum algorithmic domains.
Kapil Kumar Soni and Akhtar Rasool
IEEE
The pattern matching problem is imperative in diversified field of computer science, and it finds occurrences of the pattern string(s) of any arbitrary length “M” in the large text string of length “N”. Since past decades the solutions to a problem have been suggesting through efficient algorithms. Classical benchmark algorithms such as Knuth Morris Pratt and Boyer Moore locates such solution in O(N) time, equivalent quantum algorithms can utilize quantum computations which are inherently parallel and obtains computational speedup by providing the solution in $\\mathbf{O}(\\sqrt{\\boldsymbol{N}})$ time. The quantum pattern matching algorithm uses Grover's search logic that finds an element existence in the large unstructured text data of “N” items in $\\mathbf{O}(\\sqrt{\\boldsymbol{N}})$ time, instead classically it takes O(N) time. The article comprises of introductory pattern matching tactics, quantum basics, Grover's search method, quantum based pattern matching algorithms, their illustration followed by complexity analysis, and finally concludes with the possible algorithmic variations and relevant applications.
Jose P. Dumas, Kapil Soni, and Akhtar Rasool
Springer Singapore
Kapil Kumar Soni and Akhtar Rasool
IEEE
Cryptographic attack possibilities have several parameters and one of the possibilities is to attack over the cryptographic algorithm. Large integer factorization is still a challenging problem since the emergence of mathematics and computer science. Benchmark cryptographic protocol, the RSA Algorithm requires factorization of large integers. Classical computation does not have any polynomial time algorithm that can factor any arbitrary large integer. The remarkable but not efficient, classical algorithms for integer factorization are Trial Division, General Number Field Sieve and Quadratic Sieve. The influence of Shor’s algorithm assures to get the efficient solution of such factorization problem in polynomial time and challenges the security parameters of the existing cryptosystem, but algorithm implementation limits to be executed on a quantum computer. The article illustrates the algorithms along with flowcharts and implements, Trial Division, Quadratic Sieve Algorithm and Shor’s Algorithm for factoring integers and lastly concludes with the observed facts and analyzed results.
Kapil Kumar Soni and Vivek Sharma
IEEE
Bit Parallel String Matching algorithms are the most efficient and latest class of String Matching Algorithms. These algorithms use the intrinsic property of computer known as Bit Parallelism for performing bit operations in parallel with-in single computer word. BNDM, TNDM, BNDMq are the popular single pattern bit parallel string matching algorithms whereas Shift-OR and Shift-OR with Q-grams algorithms are popular in multiple pattern string matching. All these algorithms are restricted by the limitation of pattern size. These all algorithms are not working on patterns whose sizes are longer than computer word. In this paper, we proposed the generic method named WSR (Word Size Removal) for bit parallel string matching. With the inclusion of WSR these bit parallel string matching algorithms can work on longer than computer word size patterns. This paper presented WSR method and all modified algorithms.