Quantum circuit synthesis is a key technique in quantum computing. Traditional methods often generate circuits with high cost, making it crucial to produce correct and low-cost circuits efficiently. This study proposes the Amplitude-Ensemble Quantum-inspired Tabu Search (AE-QTS) algorithm for quantum Boolean circuit synthesis. AE-QTS demonstrates strong search capability with fewer parameters. Experimental results show that it outperforms other algorithms on low-bit problems and retains a certain level of synthesis ability on high-bit ones. However, the results also reveal that if the encoding frequently produces infeasible solutions, all algorithms will eventually fail, regardless of design. Therefore, future work should focus on developing encoding methods that consistently generate feasible solutions, which is essential for effectively applying metaheuristics to quantum Boolean circuit synthesis.

As Deep Neural Network (DNN) models became more complex, the escalating computational demands on hardware made DNN accelerators a critical research topic. The rapid growth of DNN models required DNN accelerators to keep pace with these computational demands. However, the cost of hardware design was significant, and hardware and software were tightly coupled in the design of DNN accelerators. Much research on HW/SW co-design was evident, highlighting the importance of having a comprehensive framework to help find the optimal hardware and software design during the design phase. The cost models used in most of the current research relied on data reuse and mathematical estimation to calculate costs, an approach that was fast but inaccurate. In this article, we propose a framework for HW/SW co-design and introduce a hybrid cost model based on Gem5 that provides fast and precise performance evaluation. The framework uses a memory-centric approach to accurately model off-chip memory behavior. In addition, we discuss how to find the best design in a large co-design space and integrate a design point through a traffic generator and a cost model. Finally, we demonstrate that our framework can accurately assist DNN accelerator developers in exploring the optimal hardware and software co-design quickly and efficiently.

In this study, we learned more about quantum properties from quantum and metaheuristic algorithms after conducting many experiments. We then propose an improved version of Quantum-inspired Tabu Search (QTS), which en-hances the utilization of population information. The new version is called “Amplitude-Ensemble” QTS (AE-QTS). This makes AE-QTS more similar to the real quantum search algorithm, Grover Search Algorithm, in abstract concept, while keeping the simplicity of the algorithm. Later, we demonstrate the AE-QTS on the classical combinatorial optimization 0/1 knapsack problem. Experimental results show that the AE-QTS outperforms other algorithms, including the QTS, by at least an average increase of nearly 20% in all cases and even by 30% in some cases. Even as the problem complexity increases, the quality of the solutions found by our method remains superior to that of the QTS. These results confirm that our method has better search performance.

Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain.For many years, such use involved extracting vector-space representations of words and using distances among these to predict or understand human behavior in various semantic tasks.Contemporary large language models (LLMs), however, make it possible to interrogate the latent structure of conceptual representations using experimental methods nearly identical to those commonly used with human participants.The current work utilizes three common techniques borrowed from cognitive psychology to estimate and compare the structure of concepts in humans and a suite of LLMs.In humans, we show that conceptual structure is robust to differences in culture, language, and method of estimation.Structures estimated from LLM behavior, while individually fairly consistent with those estimated from human behavior, vary much more depending upon the particular task used to generate responsesacross tasks, estimates of conceptual structure from the very same model cohere less with one another than do human structure estimates.These results highlight an important difference between contemporary LLMs and human cognition, with implications for understanding some fundamental limitations of contemporary machine language.