To further improve the performance of image retrieval, we propose an image two-classification algorithm based on continuous-time quantum walk (CTQW) model. First, a weighted undirected complete graph is constructed based on a new weighted edge method, that is, the images in the image set are used to represent the nodes on the weighted complete graph, and every two images are connected to represent the edge on the weighted complete image, the weighted value of which represents the similarity between adjacent images. Then, the CTQW is executed to realize the image two-classification based on the quantum walk model of the weighted complete graph, that is, the CTQW is performed from two types of unrelated images, and the two-classification of images is realized that the relevant and unrelated labels are assigned to each image according to the obtained probability distribution two-classification. Finally, the experimental results show that the proposed two-classification algorithm based on CTQW has a good classification effect from the visual effect, and the evaluation indexes of image classification, such as the average precision rate, the recall rate, and the accuracy rate, are all higher than 0.85. In addition, the proposed image two-classification algorithm based on CTQW can be extended to image multiclassification if the quantum walk is performed using multiple irrelevant pictures as the starting point. Similarly, the extended image multiclassification method also has a better classification effect through the experiments and the evaluation indexes of image classification.

Journal of Electronic ImagingAug 11, 20220 citations

Real-Time Adaptive Filtering and the Boxcar Limit in Superconducting Qubit Readout

Hans Johnson, Tanay Roy, Leonardo Bove +3 more

Control and readout systems for superconducting quantum computing support real-time control and high-fidelity qubit-state discrimination. We examine when additional digital processing improves readout fidelity. A common baseline for dispersive readout uses a boxcar averager to compute a uniformly weighted average over a fixed window followed by threshold-based state assignment. We evaluated an adaptive finite-impulse-response (FIR) filter trained in real time using the least-mean-squares (LMS) algorithm on a field-programmable gate array (FPGA) quantum controller. We also analyzed measured readout shots offline to compare the frozen filter with boxcar averaging, and evaluate per-sample weighting plus sequential detection. For the fixed FIR filters tested, summing the filtered samples gives the boxcar sum multiplied by one complex number apart from a small edge correction. We call this the kernel collapse. The multiplication scales the state separation and noise equally without improving state discrimination, and we call this the boxcar limit. The frozen LMS filter reduces trace noise, but shows no resolved improvement in readout fidelity. On a 2D transmon, optimizing the readout window accounted for much of the improvement over full-window integration. We then examined how changing the decision method could reduce calibration effort and decision time. On a higher-fidelity 3D ancilla readout, our modeled template estimation reached the same fidelity target as direct averaging with 89.8% fewer labeled shots per state. Offline sequential replay assigned most shots before the acquisition window ended. These results distinguish waveform denoising from state-discrimination improvement, and motivate direct control of decision weights and assignment time in dispersive readout.

quant-pharXiv:2610.00783Oct 2, 20260 citations