Statistical optimization for plant disease classification using quantum adiabatic machine learning

Abstract

Quantum Adiabatic Machine Learning (QAML) leverages principles of adiabatic quantum computation to optimize machine learning tasks such as plant disease classification. By encoding the problem into quantum Hamiltonians and using adiabatic evolution, QAML explores a vast solution space efficiently. This method uses quantum concepts such as entanglement to navigate complex energy landscapes that classical approaches find challenging. The framework applies both theoretical and practical insights to address high-dimensional data issues, essential for identifying plant diseases accurately and efficiently. We show that QAML optimizes classification tasks by encoding plant disease detection into an Ising-model-inspired Hamiltonian. The adiabatic process retains optimal configurations via multi-qubit entanglement while mitigating decoherence effects. Furthermore, we mathematically demonstrate QAML’s resilience to noise in open quantum systems and its potential quantum advantage over classical methods. This approach promises advancements in agricultural diagnostics and quantum-enhanced learning.