The pattern matching problem remains in survival since past decades and becomes more sophisticated due to exponential increase in size of text databases. An effective deterministic classical algorithm is always expect...
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The pattern matching problem remains in survival since past decades and becomes more sophisticated due to exponential increase in size of text databases. An effective deterministic classical algorithm is always expected to be at least O (N) time. Quantum computations are enough capable of performing exponential operations in single step of execution, so the quantum algorithms are effective. In general, the quantum pattern matching solution is possible in O (root N) time as its design is based on Grover's quantum search algorithm. To our knowledge, quantum algorithms for single pattern matching are available with limitations, and no algorithm has designed for multiple pattern matching. The main objective is to design quantum algorithm for both single and multiple patterns on a processing architecture of quantum random access memory (QuRAM). This gives a significant advantage to process large text databases in an efficient manner. Our complexity analysis justifies that the quantum algorithmic solutions achieve computational speedup over classical methods. We summarize the emergence of quantum-based pattern matching algorithms to process biological applications. The simulation is additionally done to validate and analyze the performance of proposed quantum algorithms. Lastly, we justify that our algorithms outperform the classical and quantum solutions and they are competent for implementing over quantum computer.
The revolution in virus genome sequencing promises to effectively map the extant biological universe and reveal fundamental relationships between viral biology, genome structure, and evolution. Indeed, microbial virus...
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The revolution in virus genome sequencing promises to effectively map the extant biological universe and reveal fundamental relationships between viral biology, genome structure, and evolution. Indeed, microbial virus genomes include large numbers of conserved coding sequences of unknown function as well as unique gene combinations, implying that that these viruses will be a significant source of novel protein biochemistry and genome architecture. Yet, making sense of the approaching phalanx of A's, G's, Ts, and C's stretching across the genome sequencing horizon will require innovation and an unprecedented coordination of annotation efforts among stakeholders. Published by Elsevier Inc.
Multiple pattern matching is Widely used in computational biology to locate any number or nucleotides in genome databases. Processing data of this size often requires more computing power than a sequential computer ca...
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ISBN:
(纸本)9789898425904
Multiple pattern matching is Widely used in computational biology to locate any number or nucleotides in genome databases. Processing data of this size often requires more computing power than a sequential computer can provide. A viable and cost-effective solution that can offer the power required by computationally intensive applications at low cost is to share computational asks among the processing nodes of a high performance hybrid distributed and shared memory platform that consists of cluster workstations and multi-core processors. This paper presents experimental results and a theoretical performance model of the hybrid implementations of the Commentz-Walter, Wu-Manber, Set Backward Oracle Matching and the Salmela-Tarhio-Kytojoki family of multiple pattern matching algorithms when executed in parallel on biological sequence databases.
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