Special-purpose and general-purpose quantum computers offer two distinct paths toward practical quantum computing applications. While both accelerate solutions to classical problems, little research has explored how integrating different quantum systems can enhance quantum computing. This work presents a novel approach that uses a coherent optical quantum computer to optimize qubit layout mapping for superconducting quantum processors. It formulates qubit mapping as a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling efficient placement of quantum circuits onto IBM’s heavy-hex topology. Using the QBoson 550w optical Ising machine to solve and post-process this model, we tested 54 quantum circuits and achieved an 88.07% reduction in SWAP gate count. This result outperforms Qiskit’s LookaheadSwap and SabreSwap routing methods by at least 21.35% when those methods are executed without prior layout optimization. Our approach surpasses TrivialLayout, DenseLayout, and SabreLayout in Qiskit, delivering at least 7.06% better optimization. Additionally, we demonstrated that the quantum approach offers notable advantages in solution success rate and efficiency, as its solution time is below the millisecond level, and it can maintain an optimization rate comparable to that of classical quadratic solvers (e.g., Gurobi and CPLEX) for the QUBO model. To our knowledge, our work is the first deep integration of optical and superconducting quantum systems, introducing a new paradigm where quantum computing optimizes itself—unlocking new possibilities for self-improving quantum architectures.

Electronic Design Automation (EDA) plays a crucial role in classical chip design and significantly influences the development of quantum chip design. However, traditional EDA tools cannot be directly applied to quantum chip design due to vast differences compared to the classical realm. Several EDA products tailored for quantum chip design currently exist, yet they only cover partial stages of the quantum chip design process instead of offering a fully comprehensive solution. Additionally, they often encounter issues such as limited automation, steep learning curves, challenges in integrating with actual fabrication processes, and difficulties in expanding functionality. To address these issues, we developed a full-stack EDA tool specifically for quantum chip design, called EDA-Q. The design workflow incorporates functionalities present in existing quantum EDA tools while supplementing critical design stages such as device mapping and fabrication process mapping, which users expect. EDA-Q utilizes a unique architecture to achieve exceptional scalability and flexibility. The integrated design mode guarantees algorithm compatibility with different chip components, while employing a specialized interactive processing mode to offer users a straightforward and adaptable command interface. Application examples demonstrate that EDA-Q significantly reduces chip design cycles, enhances automation levels, and decreases the time required for manual intervention. Multiple rounds of testing on the designed chip have validated the effectiveness of EDA-Q in practical applications.

With the widespread adoption of AArch64 architecture processors in mobile and IoT devices, ensuring the security of applications running on AArch64 systems against memory error vulnerabilities has become a critical research focus in the field of cybersecurity. However, traditional passive defense mechanisms have increasingly shown their limitations, and the growing demand for robust protection has driven continuous advancements in active defense technologies.To address the limitations of current memory protection techniques on AArch64 systems and the need for heterogeneity in heterogeneous redundant execution, we propose BinaryDMR, an efficient, selective, and lightweight runtime memory space reconfiguration tool specifically designed for source-unavailable programs.The core idea behind BinaryDMR is to statically reconstruct the runtime memory layout of source-unavailable programs, such as commercial-off-the-shelf (COTS) binaries, using static binary rewriting and library transformation techniques, including the stack, heap, code segments, and shared libraries.We have implemented BinaryDMR and evaluated its performance using a set of binary programs, including 19 kernel utilities (focused on file I/O operations) and one network daemon, Nginx (focused on network I/O operations). Experimental results demonstrate that BinaryDMR significantly reduces the success rate of common memory error-related attacks, effectively mitigates security risks arising from software homogeneity, and delivers strong practicality with minimal performance overhead.

Malware homology identification is important in attacking event tracing, emergency response scheme generation, and event trend prediction. Current malware homology identification methods still rely on manual analysis, which is inefficient and cannot respond quickly to the outbreak of attack events. In response to these problems, we propose a new malware homology identification method from a gene perspective. A malware gene is represented by the subgraph, which can describe the homology of malware families. We extract the key subgraph from the function dependency graph as the malware gene by selecting the key application programming interface (API) and using the community partition algorithm. Then, we encode the gene and design a frequent subgraph mining algorithm to find the common genes between malware families. Finally, we use the family genes to guide the identification of malware based on homology. We evaluate our method with a public dataset, and the experiment results show that the accuracy of malware classification reaches 97% with high efficiency.

Summary With the popularity of mobile devices (such as smartphones and tablets) and the development of the Internet of Things, mobile edge computing is envisioned as a promising approach to improving the computation capabilities and energy efficiencies of mobile devices. It deploys cloud data centers at the edge of the network to lower service latency. To satisfy the high latency requirement of mobile applications, virtual machines (VMs) have to be correspondingly migrated between edge cloud data centers because of user mobility. In this paper, we try to minimize the network overhead resulting from constantly migrating a VM to cater for the movement of its user. First, we elaborate on two simple migration algorithms (M‐All and M‐Edge), and then, two optimized algorithms are designed by classifying user mobilities into two categories (certain and uncertain moving trajectories). Specifically, a weight‐based algorithm (M‐Weight) and a mobility prediction–based heuristic algorithm (M‐Predict) are proposed for the two types of user mobilities, respectively. Numerical results demonstrate that the two optimized algorithms can significantly lower the network overhead of user mobility–induced VM migration in mobile edge computing environments.

The rapid popularity of mobile devices especially smart phones has changed human life style greatly. In this paper, we examine the consumer behaviors on several e-commerce platforms based on a large-scale dataset of mobile internet access records for about 3.5 months from a major telecom operator in China, which covers 126,388 users from Shanghai. We provide a preliminary study on users' daily and periodic online shopping behaviors, as well as the influence of special online shopping events and gender factors. These findings may be exploited by e-commerce providers e.g., for developing personalized recommendation systems to improve their service quality and profit.

The proliferation of mobile devices especially smart phones brings remarkable opportunities for both industry and academia. In particular, the massive data generated from users» usage logs provide the possibilities for stakeholders to know better about consumer behaviors with the aid of data mining. In this paper, we examine the consumer behaviors across multiple platforms based on a large-scale mobile Internet dataset from a major telecom operator, which covers 9.8 million users from two regions among which 1.4 million users have visited e-commerce platforms within one week of our study. We make several interesting observations and examine users» cultural differences from different regions. Our analysis shows among the multiple e-commerce platforms available, most mobile users are loyal to their favorable sites; people (60%) tend to make quick decisions to buy something online, which usually takes less than half an hour. Furthermore, we find that people in residential areas are much easier to perform purchases than in business districts and purchases take place during non-work time. Meanwhile, people with medium socioeconomic status like browsing and purchasing on e-commerce platforms, while people with high and low socioeconomic status are much easier to conduct purchases online. We also show the predictability of cross-platform shopping behaviors with extensive experiments on the basis of our observed data. Our discoveries could be a good guide for e-commerce future strategy making.