2025/09/13 by Rachana Soni, Soni, Rachana, Navneet Pratap Singh +1
Computer Science · Decision Sciences · #Amplitude #Artificial neural network #Computational Physics and Python Applications #FOS: Physical sciences #Probability amplitude #Quantum #Quantum Physics (quant-ph) #Quantum algorithm #Quantum phase estimation algorithm #Quantum probability #Scientific Computing and Data Management #Unitary state #Wave function
paper · pdf · doi:10.48550/arxiv.2509.10821
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/09/13 · openalex created_date 2025/10/12 · openalex updated_date 2026/08/05
We present an approach to simulate the Schrödinger 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.