A filter multiplexer for variable bandwidth annealing selection is described. The filter multiplexer has multiple pathways, where each pathway comprises a switch and a filter. Each filter has a different cutoff frequency from the other filters. Switches may be cryogenic switches. Each pathway may be communicatively coupled to an external annealing line. Upon receiving a problem, an annealing bandwidth can be selected, set or configured via the multiplexer to operate a quantum processor with a desired annealing schedule. The multiplexer may be used for calibration of a quantum processor by performing a calibration with a large annealing bandwidth, then calibrating the quantum processor by iterating through all available annealing bandwidths from the multiplexer.

A method includes determining a set of k extreme values of a dataset of elements in a constant time irrespective of the size of the dataset. The determining includes reviewing the values bit-by-bit, starting from the most significant bit, where bit n from each element of the dataset is reviewed at the same time.

A method includes determining a set of k extreme values of a dataset of elements in a constant time irrespective of the size of the dataset. The determining includes reviewing the values bit-by-bit, starting from the most significant bit, where bit n from each element of the dataset is reviewed at the same time.

A care area is determined in an image of a semiconductor wafer. The care area is divided into sub-care areas based on the shapes of polygons in a design file associated with the care area. A noise scan of a histogram for the sub-care areas is then performed. The sub-care areas are clustered into groups based on the noise scan of the histogram.

A care area is determined in an image of a semiconductor wafer. The care area is divided into sub-care areas based on the shapes of polygons in a design file associated with the care area. A noise scan of a histogram for the sub-care areas is then performed. The sub-care areas are clustered into groups based on the noise scan of the histogram.

Median values for a stream of received data values in a data processing system (e.g. an image processing system) are determined. A first median value of the received data values within a first subset of data values of the received stream is determined, and intermediate data used for determining the first median value is stored. The stored intermediate data is used to determine a median value of the received data values within a second subset of data values of the received stream, wherein the second subset at least partially overlaps with the first subset. The determined median values are outputted for use in the data processing system, e.g. for further processing.

A list of digital elements to be sorted are converted to a group of analog signals. The group of analog signals are simultaneously compared to each other to determine the largest analog signal in the group. The largest analog signal is then compared to each of the analog signals in the group to determine which one or more of the analog signals in the group matches the largest analog signal. The matching one or more of the analog signals is removed from the group and the process is repeated until the group of analog signals have been sorted.

A list of digital elements to be sorted are converted to a group of analog signals. The group of analog signals are simultaneously compared to each other to determine the largest analog signal in the group. The largest analog signal is then compared to each of the analog signals in the group to determine which one or more of the analog signals in the group matches the largest analog signal. The matching one or more of the analog signals is removed from the group and the process is repeated until the group of analog signals have been sorted.

A method includes determining a set of k extreme values of a dataset of elements in a constant time irrespective of the size of the dataset. A method creates a set of k indicators, each indicator associated with one multi-bit binary number in a large dataset of multi-bit binary numbers. The method includes arranging the multi-bit binary numbers such that each bit n of each said multi-bit binary number is located in a different row n of an associative memory array, starting from a row storing a most significant bit (MSB), adding an indicator to the set for each multi-bit binary number having a bit with an extreme value in the row and continuing the adding until said set contains k indicators.

A method includes determining a set of k extreme values of a dataset of elements in a constant time irrespective of the size of the dataset. A method creates a set of k indicators, each indicator associated with one multi-bit binary number in a large dataset of multi-bit binary numbers. The method includes arranging the multi-bit binary numbers such that each bit n of each said multi-bit binary number is located in a different row n of an associative memory array, starting from a row storing a most significant bit (MSB), adding an indicator to the set for each multi-bit binary number having a bit with an extreme value in the row and continuing the adding until said set contains k indicators.