Quantum computation: A quantum leap towards understanding neural information processing

Valerie Hellmer

Jincheng Zhang

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.

Mitja Peruš, Chu Kiong Loo

Hadia Azmat

Quantum computing represents a transformative leap in information processing, holding the promise to revolutionize industries through its unprecedented computational capabilities. This paper explores the evolving landscape of quantum computing by analyzing current developments, technological challenges, and emerging opportunities. It examines how quantum principles are being translated into real-world applications and identifies key obstacles to mainstream adoption, such as hardware instability, error correction, and the need for specialized quantum algorithms. By evaluating both the scientific and socio-economic implications, this paper aims to provide a comprehensive understanding of the future trajectory of quantum computing.

Vladimir G. Ivancevic, Tijana T. Ivancevic