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Quantum OPTICS and deep self-learning on swarm intelligence algorithms for Covid-19 emergency transportation
Habiba Drias1, Yassine Drias2, Naila Aziza Houacine1
1LRIA, USTHB, BP 32 El Alia Bab Ezzouar, Algiers, 16111 Algeria.
Soft Computing
|April 18, 2022
Summary
This study introduces Quantum Ordering Points To Identify the Clustering Structure (QOPTICS) for enhanced unsupervised learning. Hybridizing Quantum Machine Learning with Deep Self Learning improves emergency vehicle dispatching during the Covid-19 crisis.
Area of Science:
- Computer Science, Artificial Intelligence, Quantum Computing
Background:
- Unsupervised learning algorithms like OPTICS have real-world applications but can be computationally intensive.
- Swarm Intelligence Algorithms (SIAs) like AOA and EHO can be improved for effectiveness.
- Efficient emergency vehicle dispatching is critical, especially during crises like Covid-19.
Purpose of the Study:
- To develop a quantum-enhanced OPTICS algorithm (QOPTICS) with improved computational complexity.
- To enhance Swarm Intelligence Algorithms (SIAs) using a Deep Self-Learning (DSL) approach with dynamic mutation operators.
- To hybridize QOPTICS with DSL-enhanced SIAs for improved effectiveness and efficiency in emergency vehicle dispatching.
Main Methods:
- Designed Quantum Ordering Points To Identify the Clustering Structure (QOPTICS), a quantum-empowered density-based clustering algorithm.
- Proposed a Deep Self-Learning (DSL) approach incorporating Cauchy and Gaussian mutation operators to improve Artificial Orca Algorithm (AOA) and Elephant Herding Optimization (EHO).
- Hybridized QOPTICS with DSL-enhanced SIAs and applied to an intelligent application for emergency medical services (EMS) transportation management.
Main Results:
- QOPTICS demonstrated superior computational complexity compared to its classical counterpart.
- The hybridized approach significantly improved the effectiveness and efficiency of emergency vehicle dispatching.
- Experimental validation confirmed the positive impact of Quantum Machine Learning (QML) and DSL on solving Covid-19 EMS transportation challenges.
Conclusions:
- Quantum technology can significantly enhance unsupervised learning algorithms like OPTICS.
- Hybridizing QML with DSL offers a powerful framework for optimizing complex systems like emergency response.
- The proposed methods provide a robust solution for efficient EMS transportation management, crucial during public health crises.
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