Water quality prediction based on quantum machine learning

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Introduction Chapter I: Quantum Computing 1. Introduction 1.1. History of quantum computing 2. Double-slit experiment 3. The qubits 3.1 Example 3.2. System of qubit 4. Superposition and entanglement 4.1 Superposition 4.1. Entanglement 5. Quantum gate 5.1. The most used gates 6. Measurements 7. Quantum circuits 8. Quantum algorithms 8.2 Quantum algorithms 8.2.1 Deutsh’s Algorithm 8.2.2 Advanced Algorithms 9. IBM Qiskit 10. Quantum computer platform 11. Conclusion Chapter II: Quantum Machine Learning 1. Introduction 2. Classical Machine learning 3. Quantum Machine Learning Algorithm 3.1 Quantum neural network (QNN) 3.1.1 Basics of Quantum Neural Networks 3.1.2 Quantum Circuit Architectures for QNNs 3.2 Quantum support vector machine (QSVM) 3.2.1 Quantum feature map 3.2.2 Quantum kernel estimation 3.2.3 Quantum Variational classification 3.2.4 What is the advantage of QSVM over the classical SVM? 3.2.5 Steps for Developing a QSVM Model 3.2.6 Types of Quantum Kernels for QSVMs 3.2.7 Data Representation for QSVM Learning 4. Validation model 5. Performance Metrics 6. Conclusion Chapter III: The Water quality Classification Model 1. Introduction 2. Dataset Description and Preprocessing Phase 3. Data Encoding Phase 4. Classification Phase 5. Evaluation Phase 6. Implementation Phase in Qiskit 7. Experimental Results and Discussion 8. Conclusion Conclusion

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