Abstract: A quantum computing circuit may include a plurality of column subset qubit registers and a plurality of row subset qubit registers, a reward matrix register configured to receive a reward matrix, and an adder coupled to the column subset qubit registers, the row subset qubit registers, and the reward matrix register. The adder may be configured to perform subset summing to generate reward matrix sums from column and row subset qubits and the reward matrix. The quantum computing circuit may further include a plurality of row action qubit registers, and a comparator coupled to the adder and the row action qubit registers and configured to generate an output based thereon.

Abstract: A cognitive radio device may include a radio frequency (RF) detector operable over an RF spectrum, an RF jammer having a selectable jamming frequency window within the RF spectrum, and a controller. The controller may be configured to cooperate with the RF detector and RF jammer to detect an RF transmission, determine different Quadratic Unconstrained Binary Optimization (QUBO) inputs based upon the detected RF transmission, process the QUBO inputs with a QUBO objective function to determine a new jamming frequency window, and operate the RF jammer at the new jamming frequency window.

Abstract: A cognitive radio device may include a radio frequency (RF) detector operable over an RF spectrum, an RF transmitter having a selectable hopping frequency decoy window within the RF spectrum, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal affecting a current hopping frequency decoy window, determine different Quadratic Unconstrained Binary Optimization (QUBO) inputs based upon the detected jammer signal, process the QUBO inputs with a QUBO objective function to determine a new hopping frequency decoy window, and operate the RF decoy transmitter at the new hopping frequency decoy window.

Abstract: A cognitive radio device may include a radio frequency (RF) detector operable over an RF spectrum, an RF transmitter having a selectable hopping frequency window within the RF spectrum, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal affecting a current hopping frequency window, determine different Quadratic Unconstrained Binary Optimization (QUBO) inputs based upon the detected jammer signal, process the QUBO inputs with a QUBO objective function to determine a new hopping frequency window, and operate the RF transmitter at the new hopping frequency window.

Abstract: A computational fluid dynamics system may include a quantum computing circuit and a processor. The processor may be configured to generate and store a union mesh table representing a plurality of point groups, with at least some points being shared among different groups, and each point having a same property. The processor may be further configured to cooperate with the quantum computing circuit to perform a fluid dynamic computation based on a streaming fluid velocity vector and the union mesh table.

Abstract: An image pixel classification device may include a quantum computing circuit configured to perform quantum subset summing, and a processor. The processor may be configured to generate a pairwise game theory reward matrix for a plurality of different classes of an image pixel, with each class corresponding to a respective type of land feature from among a plurality of different types of land features, cooperate with the quantum computing circuit to perform quantum subset summing on the pairwise game theory reward matrix. The processor may further select a class for the image pixel based upon the quantum subset summing, and classify the image pixel as the corresponding type of land feature for the selected class.

Abstract: Systems and methods for operating a quantum processor. The methods comprise: training one or more quantum neural networks using modulation class data to make decisions as to a modulation classification for a signal based on one or more feature inputs for the signal; obtaining, by the quantum processor, principle components of real and imaginary components of a signal received by a communication device; and performing first quantum neural network operations by the quantum processor using the principle components as inputs to the trained one or more quantum neural networks to generate a plurality of scores, wherein each said score represents a likelihood that the received signal was modulated using a given modulation type of a plurality of different modulation types.