A Benchmarking Study of Grover’s Algorithm for Solving Boolean SAT with Quantum Circuits

This study explores how well Grover's Algorithm performs in solving the Boolean Satisfiability Problem (SAT) using quantum circuits. The algorithm is implemented with IBM's Qiskit framework and compared to classical brute-force methods. Experiments focus on 3-SAT, 4-SAT, and 5-SAT problems, using quantum simulators and IBM quantum hardware. The results show that Grover's Algorithm is more efficient, offering a theoretical quadratic speedup over classical methods. However, practical issues like limited qubit availability, hardware noise, and optimization challenges impact its current performance. The data highlights the potential for quantum computing to scale and solve NP-complete problems. This research shows how quantum computing can improve problem-solving and lays the groundwork for future studies on more complex SAT problems and advanced quantum hardware.

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Publication Details

Published
2025-05-20
DOI
https://doi.org/10.1109/ecti-con64996.2025.11100698
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
0.00

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article

A Benchmarking Study of Grover’s Algorithm for Solving Boolean SAT with Quantum Circuits

Prabhas Chongstitvatana, Jirapas U. Jipipob
Quantum Computing Algorithms and Architecture
article

A Benchmarking Study of Grover’s Algorithm for Solving Boolean SAT with Quantum Circuits

Prabhas Chongstitvatana, Jirapas U. Jipipob
article en

Abstract

This study explores how well Grover's Algorithm performs in solving the Boolean Satisfiability Problem (SAT) using quantum circuits. The algorithm is implemented with IBM's Qiskit framework and compared to classical brute-force methods. Experiments focus on 3-SAT, 4-SAT, and 5-SAT problems, using quantum simulators and IBM quantum hardware. The results show that Grover's Algorithm is more efficient, offering a theoretical quadratic speedup over classical methods. However, practical issues like limited qubit availability, hardware noise, and optimization challenges impact its current performance. The data highlights the potential for quantum computing to scale and solve NP-complete problems. This research shows how quantum computing can improve problem-solving and lays the groundwork for future studies on more complex SAT problems and advanced quantum hardware.

Chulalongkorn University (TH)
Chulalongkorn University
Openalex Percentile: Top 90%
Quantum Computing Algorithms and Architecture
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