Implementation of Grover’s Search Algorithm for Tectonic Activity Detection in an Unsorted Seismic Database
- 1Universitas Negeri Semarang
Abstract
One application of Grover’s Search Algorithm to large, unsorted databases can be found in seismic data, which is a collection of data with no specific order or structure that continues to grow over time. The Grover’s algorithm was implemented to detect tectonic activity in Indonesia based on earthquake magnitude and depth parameters. The data used consists of 5000 earthquake events in Indonesia from 2019 to 2024, obtained from United States Geological Survey (USGS). The target criteria were defined as earthquakes with a magnitude and a depth km. The research focused on the amplitude amplification mechanism through the application of a quantum oracle and a quantum diffusion, which increased the probability of finding target data in each iteration. The implementation was carried out using Python and Qiskit. Simulation results showed that both Grover’s algorithm and the classical search method successfully identified 353 target data points. However, Grover’s algorithm required only 3 iterations, whereas the classical method required 5000 iterations. The distribution of target data indicates dominant tectonic activity in North Maluku, papua, and the western Sumatra subduction zone. Grover’s algorithm further demonstrates a computational advantage thorugh a quadratic speedup, reducing the time complexity from in the classical approach to in the quantum approach.
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BibTeX
@article{mahardhika2026implementation,
title = {{Implementation of Grover’s Search Algorithm for Tectonic Activity Detection in an Unsorted Seismic Database}},
author = {Shofina Mumtaz Mahardhika and Listiyanto Listiyanto and Agus Yulianto and Mahardika Prasetya Aji},
journal = {Physics Communication},
year = {2026},
month = sep,
doi = {10.15294/pc.v10i2.59296},
url = {https://doi.org/10.15294/pc.v10i2.59296},
note = {CC BY},
}
RIS
TY - JOUR
TI - Implementation of Grover’s Search Algorithm for Tectonic Activity Detection in an Unsorted Seismic Database
AU - Shofina Mumtaz Mahardhika
AU - Listiyanto Listiyanto
AU - Agus Yulianto
AU - Mahardika Prasetya Aji
PY - 2026
DA - 2026/09/01
JO - Physics Communication
DO - 10.15294/pc.v10i2.59296
UR - https://doi.org/10.15294/pc.v10i2.59296
AB - One application of Grover’s Search Algorithm to large, unsorted databases can be found in seismic data, which is a collection of data with no specific order or structure that continues to grow over time. The Grover’s algorithm was implemented to detect tectonic activity in Indonesia based on earthquake magnitude and depth parameters. The data used consists of 5000 earthquake events in Indonesia from 2019 to 2024, obtained from United States Geological Survey (USGS). The target criteria were defined as earthquakes with a magnitude and a depth km. The research focused on the amplitude amplification mechanism through the application of a quantum oracle and a quantum diffusion, which increased the probability of finding target data in each iteration. The implementation was carried out using Python and Qiskit. Simulation results showed that both Grover’s algorithm and the classical search method successfully identified 353 target data points. However, Grover’s algorithm required only 3 iterations, whereas the classical method required 5000 iterations. The distribution of target data indicates dominant tectonic activity in North Maluku, papua, and the western Sumatra subduction zone. Grover’s algorithm further demonstrates a computational advantage thorugh a quadratic speedup, reducing the time complexity from in the classical approach to in the quantum approach.
SN - 2528-5971
SN - 2528-598X
ER -