We recognize many computational tasks typically thought to require quantum computers are low rank and can be accomplished on classical computers with quantics tensor trains. This led to development of the Iterative Power Algorithm (IPA): a generalization of imaginary time propagation that locates global minima of discrete and continuous functions. In chemistry, this has practical applications in molecular geometry optimization and protein folding. We demonstrate the success of the classical method despite high energy barriers and local minima traps.

The Iterative Power Algorithm: A Quantum Superposition Approach to Global Optimization

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