Simulating and Learning Quantum Evolution: A CTQW-ML Framework

• URL: http://arxiv.org/abs/2509.10821v1
• Date: Sat, 13 Sep 2025 14:39:47 GMT
• Title: Simulating and Learning Quantum Evolution: A CTQW-ML Framework
• Authors: Rachana Soni, Navneet Pratap Singh,
• Abstract summary: We present an approach to simulate the Schr"odinger equation through continuous time quantum walks.<n>We implement a supervised neural network model to evaluate the effectiveness of data-driven techniques.
• Score: 1.7188280334580195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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