Neural Network Quantum Leap (NNQL)

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Neural Network Quantum Leap (NNQL) is a novel approach to quantum computing that leverages deep learning to predict quantum system trajectories and automatically adjust quantum circuits. Traditional quantum computation struggles with the unpredictable nature of quantum transitions, leading to inefficient algorithms. NNQL aims to overcome this limitation by building a deep learning model to forecast quantum跃ations and optimize circuits in real-time. This research presents a framework for predicting quantum transitions using neural networks, demonstrating a significant improvement in quantum circuit efficiency and overall system performance. The core mechanism hinges on learning from historical data to model quantum transition probabilities, enabling automated circuit optimization. We demonstrate the effectiveness of NNQL through simulations and analysis of quantum circuit dynamics, showcasing a substantial increase in expected quantum output with minimal control parameter adjustments.

DOI: 10.5281/zenodo.22486273

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