Quantum Machine Learning
Adiabatic quantum computing is a model of quantum computation that utilizes the principles of quantum mechanics to solve optimization problems by gradually transforming a simple initial Hamiltonian into a final Hamiltonian representing the solution. This approach relies on the adiabatic theorem, which states that a system remains in its ground state if the changes to its Hamiltonian are made slowly enough. This method is closely related to concepts such as quantum annealing, complexity theory, and the broader implications of quantum speedup in problem-solving.
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