Abstract Combinatorial optimization problems over graphs, such as the traveling salesman problem, longest path problem, and maximum independent set problem, are well-known for being computationally costly, some even NP-hard problems. In this paper, we propose a general quantum algorithm framework searching for approximate solutions to combinatorial optimization problems with linear objective functions. Our framework provides APIs (application programming interfaces) that enable developers to encode weighted graph structures onto quantum circuits and utilize variational algorithms to generate approximate solutions. One key advantage of our framework is that it allows developers to design new graph algorithms for the graph problem represented as linear combinations of edge weights without requiring expertise in quantum programming. Besides, it only uses a logarithmic level of quantum bit scale, making our framework work on quantum computers with limited physical resources. Our experimental results demonstrate that our framework can provide good approximations for the traveling salesman problem compared to current quantum algorithm.

Quantum computing represents a next-generation technology in data processing, promising to transcend the limitations of traditional computation. In this paper, we undertake an early exploration of the potential integration of quantum computing with database query optimization. We introduce a pioneering hybrid classical-quantum algorithm for sampling-based approximate query processing (AQP). The core concept of the algorithm revolves around identifying rare groups, which often follow a long-tail distribution, and applying distinct sampling methodologies to normal and rare groups. By leveraging the quantum capabilities of the diffusion gate and QRAM, the algorithm defines a novel quantum sampling approach that iteratively amplifies the signals of these infrequent groups. The algorithm operates without the need for preprocessing or prior knowledge of workloads or data. It utilizes the power of quadratic acceleration to achieve well-balanced sampling across various data categories. Experimental results demonstrate that in the context of AQP, the new sampling scheme provides higher accuracy at the same sampling cost. Additionally, the benefits of quantum computing become more pronounced as query selectivity increases.

Many IoT applications require the use of multiple deep neural networks (DNNs) to perform various tasks on low-cost edge devices with limited computation resources. However, existing DNN model serving platforms, such as TensorFlow Serving and TorchServe, are resource-intensive and require high-performance GPUs that are often not available on low-cost edge devices. In this paper, we propose SmartLite, a lightweight DBMS that addresses these challenges by storing the parameters and structural information of neural networks as database tables and implementing neural network operators inside the DBMS engine. SmartLite quantizes model parameters as binarized values, applies neural pruning techniques to compress the models, and transforms tensor manipulations into value lookup operations of the DBMS to reduce computation overhead. Experimental results show that SmartLite requires 98% less memory while achieving about a 134% performance speedup compared to Torch-Serve. Our proposed solution addresses the challenges of running multiple DNN models on low-cost edge devices and provides a significant contribution to the field of IoT applications.

This paper investigates the usage of the projection distance and the pseudo Bayes discriminant function as the distortion measure for handwritten numeral clustering problem. These distortion measures not only refer to the mean vectors but are also related to the covariance matrixes of subclasses, thus, the distribution of subclasses are reflected on the obtained clusters, and the accuracy of recognition can be improved. A series of evaluation experiments are performed on the handwritten numeral database NIST SD3 and SD7. The experimental results show that the recognition rate has been increased from 97.35% to 98.35%, which is one of the highest rates ever reported for the database.

This paper proposes a new corrective learning algorithm and evaluates the performance by a handwritten numeral recognition test. The algorithm generates a mirror image of a pattern that belongs to one class of a pair of confusing classes and utilizes it as a learning pattern of the other class. This paper also studies how to extract confusing patterns within a certain margin of a decision boundary to generate enough mirror images, and how to perform an effective mirror image compensation to increase the margin. Recognition accuracies of the minimum distance classifier and the projection distance method were improved from 93.17% to 98.38% and from 99.11% to 99.41% respectively in the recognition test for handwritten numeral database IPTP CD-ROM1.

Studies the use of curvature in addition to the gradient of gray-scale character images in order to improve the accuracy of handwritten numeral recognition. Three procedures, based on the curvature coefficient, biquadratic interpolation and gradient vector interpolation, are proposed for calculating the curvature of the equi-gray-scale curves of an input image. The efficiency of the feature vector is tested by recognition experiments for the handwritten numeral database IPTP CDROM1, which is a ZIP code database provided by the Institute for Posts and Telecommunications Policy (IPTP). The experimental results show the usefulness of the curvature feature, and a recognition rate of 99.40%, which is the highest that has ever been reported for this database, is achieved.