Why Should We Bother with Quantum Computing in ML?

Quantum computing is presented as a solution to the physical limitations of classical computing, particularly the breakdown of Moore's Law. It leverages principles like superposition, entanglement, and interference to process information with qubits, offering potential speedups for complex machine learning and optimization problems. The technology also poses a significant threat to current encryption standards, leading to the development of quantum-resistant cryptographic methods. By using quantum mechanics, certain computationally hard problems, like factoring large numbers, could become solvable, fundamentally changing fields like security and data science. Although the hardware is still emerging, the conceptual foundations of quantum machine learning (QML) are being established to overcome future computational bottlenecks.