Tell me what is quantum machine learning. Explain me with needful animation and also say how it is different from traditional or classical machine learning

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Quantum Machine Learning is an exciting interdisciplinary field that merges quantum computing with machine learning. It leverages quantum mechanical properties like superposition and entanglement to potentially solve complex computational problems much faster than classical computers. QML represents the intersection of two revolutionary technologies. The fundamental difference between classical and quantum computing lies in how information is stored. Classical computers use bits that are either zero or one. Quantum computers use qubits, which can exist in a superposition of both zero and one states simultaneously. This is visualized using the Bloch sphere, where any point on the sphere represents a valid quantum state. Quantum entanglement is a unique quantum phenomenon where two or more qubits become correlated in such a way that the quantum state of each qubit cannot be described independently. When qubits are entangled, measuring one qubit instantly affects the state of its entangled partner, regardless of the distance between them. This property is fundamental to quantum machine learning algorithms. The key differences between classical and quantum machine learning lie in their computational approaches. Classical machine learning processes information sequentially using traditional algorithms, while quantum machine learning leverages quantum parallelism to potentially achieve exponential speedups. Classical ML is mature and widely deployed, whereas quantum ML is still in early development but promises revolutionary capabilities for complex optimization and pattern recognition tasks. Quantum machine learning represents the future of computational intelligence. While we are currently in the early stages with noisy intermediate-scale quantum devices, the potential applications are vast, including drug discovery, financial modeling, and complex optimization problems. The main challenges include hardware limitations and quantum error correction. As quantum computers become more powerful and fault-tolerant, QML will likely revolutionize how we approach machine learning and artificial intelligence.