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Springer eBooks

Application of Adaptive Dynamics to Biology

Masanari Asano, Andrei Yu. Khrennikov, Masanori Ohya, Yoshiharu Tanaka, Ichiro Yamato

Published Jan 1, 2015• 0 citations
Publisher
DOI
10.1007/978-94-017-9819-8_5

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The simulation of complex biological systems, such as protein folding and gene regulation, presents significant challenges due to the inherent complexity and often intractable nature of these systems. Traditional computational methods struggle to capture the nuanced dynamics of biological processes, limiting our ability to understand and potentially manipulate them. This research proposes an innovative approach – adaptive quantum simulation – that leverages the principles of quantum mechanics to create dynamic, self-adjusting simulations of biological systems. We aim to develop an algorithm that continuously refines simulation parameters, automatically mimicking biological behavior to achieve unprecedented accuracy and fidelity. This work explores the potential of quantum computation to overcome limitations inherent in classical simulation techniques, offering a fundamentally new pathway for biological system modeling and analysis. This includes a detailed explanation of the algorithm's core mechanisms, potential applications, and preliminary results demonstrating its adaptability. The core claim is that this adaptive quantum simulation method will allow for a level of detail and accuracy previously unattainable through conventional computational methods.
Quantum simulation offers a revolutionary approach to modeling complex biological systems, particularly metabolic pathways, which currently demand immense computational resources. This paper explores the application of quantum computing to simulate these pathways, focusing on the development of a novel method for dynamically adapting quantum states to mimic the intricate changes inherent within metabolic processes. We propose a quantum circuit design that mimics the key steps of a metabolic pathway, leveraging adaptive quantum states to represent and evolve the energy levels, thereby providing a pathway to significantly enhance the fidelity and accuracy of simulations. The core claim is that this method will enable a more thorough investigation of metabolic dynamics, offering unprecedented insights into biological function and potential therapeutic targets.