Hybrid quantum neural networks: harnessing dressed quantum circuits for enhanced tsunami prediction via earthquake data fusion

Abstract

Tsunami is one of the deadliest natural disasters which can occur, leading to great loss of life and property. This study focuses on predicting tsunamis, using earthquake dataset from the year 1995 to 2023. The research introduces the Hybrid Quantum Neural Network (HQNN), an innovative model that combines Neural Network (NN) architecture with Parameterized Quantum Circuits (PmQC) to tackle complex machine learning (ML) problems where deep learning (DL) models struggle, aiming for higher accuracy in prediction while maintaining a compact model size. The hybrid model’s performance is compared with the classical model counterpart to investigate the quantum circuit’s effectivity as a layer in a DL model. The model has been implemented using 2-6 features through Principle Component Analysis (PCA) method. HQNN’s quantum circuit is a combination of Pennylane’s embedding (Angle Embedding (AE) and Instantaneous Quantum Polynomial (IQP) Embedding) and layer circuits (Basic Entangler Layers (BEL), Random Layers (RL), and Strongly Entangling Layers (SEL)), along with the classical layers. Results show that the proposed model achieved high performance, with a maximum accuracy up to 96.03% using 4 features with the combination of AE and SEL, superior to the DL model. Future research could explore the scalability and diverse applications of HQNN, as well as its potential to address practical ML challenges.