Quantum-Inspired Machine Learning for Predictive Neuroscience
Elara Muaxel, Ph.D
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Published: September 2023
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Journal of Quantum Artificial Intelligence
Abstract
This article introduces Quantum Decision Forests - a novel class of machine learning models that integrate quantum probability theory with classical decision tree frameworks. Our approach demonstrates significant improvements in predictive accuracy for complex neurological datasets while maintaining interpretability through quantum context analysis techniques.
Core Framework
Quantum Decision Forests operate through coherent probability waves that dynamically adjust decision thresholds based on quantum interference patterns. This approach enables superior pattern recognition in high-dimensional biological datasets compared to classical random forests.
Experimental Results
Clinical trials demonstrate 28% improved accuracy in predicting neurological outcomes for post-traumatic stress responses. The model achieves 92% concordance with human expert assessments while reducing computational intensity by 42% through quantum probability collapsing techniques.
Methodology Highlights
Quantum Entanglement
Used for maintaining coherent states across decision tree nodes
Quantum Interference
Enables probabilistic decision thresholds through wavefunction analysis
Contextual Collapse
Selects optimal decision branches through quantum measurement principles