Simulating and Learning Quantum Evolution: A CTQW-ML Framework
Abstract
We present an approach to simulate the Schr\"odinger equation through continuous time quantum walks. The CTQW-based simulation applies unitary evolution driven by a quantum walk to generate probability amplitude distributions at various time steps. Additionally, we implemented a supervised neural network model to evaluate the effectiveness of data-driven techniques. The model learns to predict the squared modulus of the wavefunction given spatial and temporal coordinates. A comparative analysis demonstrates that the ML model can reproduce the qualitative structure and temporal progression of the quantum system with high accuracy. This study provides the synergy between quantum walk-based simulation and machine learning for solving quantum dynamical equations.
Cite
@article{arxiv.2509.10821,
title = {Simulating and Learning Quantum Evolution: A CTQW-ML Framework},
author = {Rachana Soni and Navneet Pratap Singh},
journal= {arXiv preprint arXiv:2509.10821},
year = {2025}
}