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.

Quantum compilers play an essential role in the translation of quantum algorithms on quantum hardware. As the size of quantum programs increases, the problem of extreme compilation overhead becomes apparent. To address this problem, compilation frameworks with high-level intermediate representations such as QCOR have been introduced. However, these frameworks still face significant overhead from qubit mapping and optimizing sequence redundancy, resulting in challenges when compiling circuits to realistic hardware. In our paper, a low-cost optimizing compiler (LCOC) is proposed, which addresses the overhead in qubit mapping and enhanced the optimization capabilities. A qubit mapping optimizer based on the LLVM Polly is introduced in LCOC, which implements an efficient mapping of quantum programs containing specific loop types. While achieving low-cost mapping, it considerably reduces the depth of the circuit. We also implement an optimization sequence generator that leverages the modularity of LLVM and iterative compilation to provide LCOC with a more flexible and efficient optimization sequence. We conduct experiments with 100+ qubit circuits. Experiments show that LCOC is up to 1540× faster compared to Qiskit and 74× faster compared to QCOR in compile time. The average speedup relative to Qiskit and QCOR are 194× and 8.64×, respectively. In addition, it can effectively reduce the circuit depth by 50.32% compared to Qiskit and 83.5% compared to QCOR on average.

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.

To address emerging security threats, various malware detection methods have been proposed every year. Therefore, a small but representative set of malware samples are usually needed for detection model, especially for machine-learning-based malware detection models. However, current manual selection of representative samples from large unknown file collection is labor intensive and not scalable. In this paper, we firstly propose a framework that can automatically generate a small data set for malware detection. With this framework, we extract behavior features from a large initial data set and then use a hierarchical clustering technique to identify different types of malware. An improved genetic algorithm based on roulette wheel sampling is implemented to generate final test data set. The final data set is only one-eighteenth the volume of the initial data set, and evaluations show that the data set selected by the proposed framework is much smaller than the original one but does not lose nearly any semantics.

Aiming at the limitations of benchmark tools, the paper brings a model, which is beneficial for NP-based application design, and which ought to be obeyed on the four levels of system, function, task and hardware. Then five formulas are given specifically for Intel NPbased application design. In the third part, an study case and it’s testing result are given with these disciplines and formulas applied. Finally the paper points out under which circumstances can these formulas be applied.