Implementation of Grover’s Search Algorithm for Tectonic Activity Detection in an Unsorted Seismic Database

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.