Function Maximization with Dynamic Quantum Search
Finding the maximum value of a function in a dynamic model plays an important role in many application settings, including discrete optimization in the presence of hard constraints. We present an iterative quantum algorithm for finding the maximum value of a function in which prior search results update the acceptable response. Our approach is based on quantum search and utilizes a dynamic oracle function to mark items in a specified input set. As a realization of function optimization, we verify the correctness of the algorithm using numerical simulations of quantum circuits for the Knapsack problem. Our simulations make use of an explicit oracle function based on arithmetic operations and a comparator subroutine, and we verify these implementations using numerical simulations up to 30 qubits.
KeywordsMaximization Quantum search Quantum optimization
CM and HC acknowledge support from Total and TSH acknowledges support from the U.S. Department of Energy, Office of Science, Early Career Research Program. Access to the Atos Quantum Learning Machine was provided by the Quantum Computing Institute at Oak Ridge National Laboratory.
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