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.

Recent years have witnessed a tremendous amount of interest in wireless sensing, i.e., instead of employing traditional sensors, wireless signal is utilized for sensing purposes. Contact-free wireless sensing has been successfully demonstrated using various RF signals such as WiFi, RFID, LoRa, and mmWave, enabling a large range of applications. However, limited by hardware thermal noise, the granularity of RF sensing is still relatively coarse. In this paper, instead of using the macro signal power/phase for sensing, we propose the first quantum wireless sensing system, which uses the micro energy level of atoms for sensing, improving the sensing granularity by an order of magnitude. The proposed quantum wireless sensing system is capable of utilizing a wide spectrum of frequencies (e.g., 2.4 GHz, 5 GHz and 28 GHz) for sensing. We demonstrate the superior performance of quantum wireless sensing with two widely-used signals, i.e., WiFi and 28 GHz millimeter wave. We show that quantum wireless sensing can push the sensing granularity of WiFi from millimeter level to sub-millimeter level and push the sensing granularity of millimeter wave to micrometer level.

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 focuses on locally microscopic pedestrian walking behavior and proposes an intelligent behavioral learning approach for its prediction. In this approach, pedestrian walking behavior was modeled as a special artificial neural network whose input and output layers were used to accommodate a pedestrian's perceived environmental information (e.g., information on the destination, obstacles, and neighbors) and his or her walking behavioral response, respectively. The developed neural network was trained based on a large volume of data samples of real-life pedestrian walking behavior (3813 training samples) to acquire knowledge of pedestrian walking behavior and to develop the ability to predict microscopic pedestrian walking behavior. A quantitative evaluation index, R-squared, was calculated to evaluate the learning performance; the mean was calculated as 0.900, which indicates that the neural network can well capture the underlying decision-making mechanism behind pedestrian walking behavior. The approach was verified by the prediction of microscopic pedestrian walking behavior details in two real-life scenarios. The vector displacement error and the speed error were calculated to evaluate the quality of the prediction; the mean of the vector displacement error (the speed error) in two scenarios were, respectively, calculated as 0.192 (0.116) and 0.226 (0.096), which indicates that the prediction results were acceptable from an engineering perspective. Based on these results, we consider the developed approach to be capable of predicting microscopic pedestrian walking behavior. Moreover, extended application also shows that the proposed approach has promise for simulation of the short-term flow of a pedestrian crowd.

The efficiency of bandwidth scheduling in high-performance networks (HPNs) is critical to the utilization of network resources and the satisfaction of user requests. We consider a periodic bandwidth scheduling problem to maximize the number of satisfied fixed-slot bandwidth reservation requests, referred to as multiple fixed-slot bandwidth scheduling (MFSBS), which is shown to be NP-complete. We first design a minimum resource occupation algorithm for a special type of M-FSBS with identical slots, referred to as MinRO-IS, and further propose a generalized version of MinRO for M-FSBS with arbitrary slots. We also design four greedy algorithms for performance comparison. Extensive simulation results illustrate that both MinRO-IS and MinRO have a superior performance over the existing algorithms in the literature and the other four greedy algorithms in comparison. Considering the popularity of the FSBS-based service model and the rapid expansion of HPNs in both speed and scope, the proposed scheduling algorithms have great potential to improve the network performance of big-data applications that require the FSBS service in HPNs.

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.