139 papers indexed here
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This paper explores the potential of quantum computing to revolutionize bioinformatics, specifically focusing on enhanced gene sequence analysis and protein structure prediction. The core concept centers around utilizing quantum error correction and quantum computation to accelerate these computationally intensive task...
This paper proposes a novel approach to protein structure prediction leveraging principles from quantum mechanics, specifically entanglement and superposition. Traditional methods for protein folding, reliant on classical computational techniques, frequently struggle with accuracy and efficiency, particularly when deal...
Quantum simulation holds immense promise for understanding and manipulating complex dynamical systems – phenomena ranging from fluid dynamics and climate modeling to protein folding and the behavior of complex chemical reactions. However, current simulation techniques face significant limitations, particularly when dea...
The field of topology has witnessed significant advancements in understanding and manipulating complex network structures, from protein folding to genome sequencing. This research introduces a novel approach to topology optimization – the "Adaptive Topology Optimization Algorithm" – that leverages a self-adaptive topol...
Quantum error correction is a critical component of quantum computing, enabling the reliable operation of complex quantum algorithms. Current error correction schemes, however, are often cumbersome and require substantial overhead. This paper explores a novel quantum error correction scheme inspired by the self-correct...
Quantum error correction is a critical component of quantum computing, enabling the reliable operation of complex quantum algorithms. Current error correction schemes, however, are often cumbersome and require substantial overhead. This paper explores a novel quantum error correction scheme inspired by the self-correct...
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 abilit...
This paper explores the potential of utilizing geometric analysis as a novel approach to understanding the complexity of biological systems. Biological systems, particularly protein folding and gene regulation, exhibit intricate geometric structures. This research proposes a framework to quantify and analyze these stru...
This paper explores the potential of utilizing geometric analysis as a novel approach to understanding the complexity of biological systems. Biological systems, particularly protein folding and gene regulation, exhibit intricate geometric structures. This research proposes a framework to quantify and analyze these stru...
Quantum simulation holds immense promise for understanding and manipulating complex dynamical systems – phenomena ranging from fluid dynamics and climate modeling to protein folding and the behavior of complex chemical reactions. However, current simulation techniques face significant limitations, particularly when dea...
Bayesian Geometric Chaos with Adaptive Constraint Propagation represents a novel approach to modeling complex systems, particularly those exhibiting intricate dynamics and high-dimensional parameter spaces. This paper explores the integration of a variational Bayesian framework, incorporating adaptive constraint propag...
This paper proposes a novel approach to computing, termed Dynamic Semantic Network Neuro-Morphic Computing, which leverages the principles of biological neural networks to achieve parallel, adaptive learning, and reasoning for complex data structures. The core idea is to mimic the dynamic connectivity and synaptic plas...
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