A quantum convolutional neural network on NISQ devices

Quantum machine learning is one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks (CNN), which greatly reduces the computing complexity compared with its classical counterparts, with O((log2M)6) basic gates and O(m2+e) variational parameters, where M is the input data size, m is the filter mask size, and e is the number of parameters in a Hamiltonian. Our model is robust to certain noise for image recognition tasks and the parameters are independent on the input sizes, making it friendly to near-term quantum devices. We demonstrate QCNN with two explicit examples. First, QCNN is applied to image processing, and numerical simulation of three types of spatial filtering, image smoothing, sharpening, and edge detection is performed. Secondly, we demonstrate QCNN in recognizing image, namely, the recognition of handwritten numbers. Compared with previous work, this machine learning model can provide implementable quantum circuits that accurately corresponds to a specific classical convolutional kernel. It provides an efficient avenue to transform CNN to QCNN directly and opens up the prospect of exploiting quantum power to process information in the era of big data.

Multimedia Motion of Picking Robot Based on Cooperative Relationship of Matching Gradient Algorithm

With the continuous development of social economy, robots gradually replace human beings in many aspects of auxiliary work, but it is worth noting that orderly, accurate, and safe operation is the reasonable form of robot movement. In view of the existing limitations, this study combines the gradient matching algorithm, by using a high-precision motion matching for the visual system, improves the automation precision, and, at the same time, picking fruit image smoothing and enhancement, achieves fruit edge feature extraction; the simulation experiments proved that matching the gradient algorithm is effective and can realize the visual and actions match together and support picking robot movement.

A High-Speed and Power-Efficient Approximate Adder for Image Processing Applications

Most image processing applications are naturally imprecise and can tolerate computational error up to a specific limit. In such applications, savings in power are achieved by pruning the data path units, such as an adder module. Truncation, however, may lead to errors in computing, and therefore, it is always a challenge between the amount of error that can be tolerated in an application and savings achieved in area, power and delay. This paper proposes a segmented approximate adder to reduce the computation complexity in error-resilient image processing applications. The sub-carry generator aids in achieving a faster design while carry speculation method employed improves the accuracy. Synthesis results indicate a reduced die-area up to 36.6%, improvement in delay up to 62.9%, and reduction in power consumption up to 34.1% compared to similar work published previously. Finally, the proposed adder is evaluated by using image smoothing and sharpening techniques. Simulations carried out on these applications prove that the proposed adder obtains better peak signal-to-noise ratio than those available in the literature.

Image smoothing based on global sparsity decomposition and a variable parameter

Smoothing images, especially with rich texture, is an important problem in computer vision. Obtaining an ideal result is difficult due to complexity, irregularity, and anisotropicity of the texture. Besides, some properties are shared by the texture and the structure in an image. It is a hard compromise to retain structure and simultaneously remove texture. To create an ideal algorithm for image smoothing, we face three problems. For images with rich textures, the smoothing effect should be enhanced. We should overcome inconsistency of smoothing results in different parts of the image. It is necessary to create a method to evaluate the smoothing effect. We apply texture pre-removal based on global sparse decomposition with a variable smoothing parameter to solve the first two problems. A parametric surface constructed by an improved Bessel method is used to determine the smoothing parameter. Three evaluation measures: edge integrity rate, texture removal rate, and gradient value distribution are proposed to cope with the third problem. We use the alternating direction method of multipliers to complete the whole algorithm and obtain the results. Experiments show that our algorithm is better than existing algorithms both visually and quantitatively. We also demonstrate our method’s ability in other applications such as clip-art compression artifact removal and content-aware image manipulation.