University of Southern California

Walter Vinci

Daniel A Lidar

Tameem Albash

An optimally stopped heuristic optimizer includes a processor. The processor heuristically generates a sequence of proposed solutions for solving a discrete optimization problem associated with an objective function, and for each additional proposed solution of the sequence, derives an estimate of a quality distribution that is based on the sequence including the additional proposed solution. The quality distribution assigns a probability to each of the proposed solutions according to quality of the proposed solution. The processor further, responsive to a most recent additional proposed solution of the sequence having quality better than a quality threshold that is defined by the estimate corresponding to the sequence including the most recent additional proposed solution, commands a stop such that further proposed solutions to the discrete optimization problem are no longer generated, and identifies and outputs a selected one of the sequence having best quality.

Optimally stopped optimization systems having heuristic optimizer and methods using the same

Systems and methods of processing using a quantum processor are described. A method includes obtaining a problem Hamiltonian and defining a nested Hamiltonian with a plurality of logical qubits by embedding a logical Krepresenting the problem Hamiltonian into a larger K, where N represents a number of the logical qubits and C represents a nesting level defining the amount of hardware resources for the nest Hamiltonian. The method also includes encoding the nested Hamiltonian into the plurality of physical qubits of the quantum processor; and performing a quantum annealing process with the quantum processor after the encoding.

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Claremont, CA, United States of America

Walter Vinci