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Lecture notes in networks and systems

A Comparative Analysis of Grover's Algorithm with Classical Algorithms

Issac Diana Jeba Jingle, Sharlene Anna Pereira

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-981-95-3163-9_27

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Contemporarily, quantum computing is one of the hottest research fields. Many quantum algorithms are proposed in order to utilize the power of quantum computers. Grover’s searching algorithm is one of them. In this article, by comparing a classical searching algorithm and Grover’s algorithm in the problem of finding a number in a finite database, the advantages of the latter are discussed. The actual quantum circuit to solve the problem is built and run on both a simulator and a real quantum computer. According to the analysis, Grover’s algorithm provides speedup in a searching task compared to the classical algorithm. However, noises in today’s quantum devices make the result of the quantum algorithm unreliable. In searching for multiple numbers, Grover’s algorithm has its shortcomings. Nevertheless, noises in quantum computing need to be addressed in order to utilize the potential of quantum computers in solving difficult problems. These results shed light on guiding further exploration of quantum algorithms and quantum computing.
The study of protein-protein interactions (PPIs) and predicting the protein structure plays a critical role in understanding cellular processes and designing therapeutic interventions. In this research, we explore the application of quantum algorithms, specifically Grover’s algorithm, in improving the accuracy and efficiency of PPI prediction. By harnessing the inherent parallelism and quantum search capabilities of Grover’s algorithm, we aim to enhance the identification of interacting protein pairs from large-scale datasets. We demonstrate the effectiveness of using this algorithm through an extensive approach, comparing the performance of Grover’s algorithm with classical machine learning algorithms. Our results reveal that the quantum algorithm offers significant improvements in prediction accuracy, enabling the identification of previously undetected PPIs. Moreover, we discuss the advantages and limitations of using Grover’s algorithm in PPI prediction and provide insights into its potential for accelerating research in protein interaction networks. This research highlights the potential of quantum algorithms in advancing the field of bioinformatics and protein interaction analysis.
This paper evaluates eight computational algorithms across sorting, searching, and factoring tasks to analyze how their operation counts impact environmental sustainability. Classical algorithms (Bubble Sort, Insertion Sort, Merge Sort, Timsort, Quicksort, Linear Search) are compared against quantum algorithms (Grover's Search, Shor's Factoring) using algorithmic time complexity as a proxy for processing energy consumption. The analysis establishes a distinct efficiency hierarchy determined by problem scale and mathematical scaling behavior. For quadratic speedups like Grover's Search, a high problem-size threshold exists before quantum algorithms become more energy-efficient than classical alternatives. For exponential speedups like Shor's Algorithm, the quantum framework crosses this threshold almost immediately due to the impossibly large operation counts required by classical alternatives at cryptographic scales. These findings demonstrate that achieving sustainable quantum computing requires targeted deployment at exponential problem domains.