Quantum Machine Learning
Quantum Machine Learning Abstract
- Introduction to Quantum Machine Learning ● History of QML
- Foundational Concepts in Quantum Computing for ML ● Quantumgates, circuits, and measurements ● Quantumalgorithms and their relevance to ML ● Keyquantum computing frameworks (e.g., IBM Qiskit, Google’s Cirq, Xanadu’s PennyLane)
- Quantum Machine Learning Demos using Pennylane and Qiskit: ● Learning Shallow Quantum Circuits with Local Operations ● Generalization in QML from few training data
- Case Study: QCNN v/s classical CNN
- Challenges and Limitations in QML ● Noiseand decoherence in quantum computers ● Limited qubit counts and gate fidelity issues in current quantum hardware ● Highcomputational costs for simulation on classical systems
- Conclusion and Summary ● Summaryof the potential impact of QML on both machine learning and quantum computing ● Discussion of expected advancements and how they might reshape the landscape of AI