Quantum computing, leveraging principles of quantum mechanics, represents a
transformative approach in computational methodologies, offering significant
enhancements over traditional classical systems. This study tackles the complex
and computationally demanding task of simulating power system transients
through solving differential algebraic equations (DAEs). We introduce two novel
Quantum Neural Networks (QNNs): the Sinusoidal-Friendly QNN and the
Polynomial-Friendly QNN, proposing them as effective alternatives to
conventional simulation techniques. Our application of these QNNs successfully
simulates two small power systems, demonstrating their potential to achieve
good accuracy. We further explore various configurations, including time
intervals, training points, and the selection of classical optimizers, to
optimize the solving of DAEs using QNNs. This research not only marks a
pioneering effort in applying quantum computing to power system simulations but
also expands the potential of quantum technologies in addressing intricate
engineering challenges.

本研究旨在应用量子计算技术解决电力系统仿真问题，通过引入两种新型量子神经网络（Sinusoidal-Friendly QNN 和 Polynomial-Friendly QNN）来优化解决微分代数方程的传统模拟技术，并成功地在两个小型电力系统中应用这些量子神经网络进行仿真，表明其在精度上具有潜力，且进一步研究了各种配置来优化量子神经网络解决微分代数方程的效果，这不仅是量子计算在电力系统模拟中的首次尝试，还拓展了量子技术在解决复杂工程问题上的潜力。

量子神经网络解决电力系统暂态仿真问题

Quantum Neural Networks for Solving Power System Transient Simulation  Problem

Biologically Inspired Design (BID), or Biomimicry, is a problem-solving
methodology that applies analogies from nature to solve engineering challenges.
For example, Speedo engineers designed swimsuits based on shark skin. Finding
relevant biological solutions for real-world problems poses significant
challenges, both due to the limited biological knowledge engineers and
designers typically possess and to the limited BID resources. Existing BID
datasets are hand-curated and small, and scaling them up requires costly human
annotations.
In this paper, we introduce BARcode (Biological Analogy Retriever), a search
engine for automatically mining bio-inspirations from the web at scale. Using
advances in natural language understanding and data programming, BARcode
identifies potential inspirations for engineering challenges. Our experiments
demonstrate that BARcode can retrieve inspirations that are valuable to
engineers and designers tackling real-world problems, as well as recover famous
historical BID examples. We release data and code; we view BARcode as a step
towards addressing the challenges that have historically hindered the practical
application of BID to engineering innovation.

通过自然界的生物学类比来解决工程挑战的生物启发式设计（BID）或仿生学是一种解决问题的方法，而 BARcode 是一个用于在网络上自动挖掘生物启发式设计的搜索引擎，能够识别潜在的工程挑战的启发源。

模仿生命：生物启发设计的搜索引擎

Imitation of Life: A Search Engine for Biologically Inspired Design

We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language
model with 130 billion parameters. It is an attempt to open-source a 100B-scale
model at least as good as GPT-3 and unveil how models of such a scale can be
successfully pre-trained. Over the course of this effort, we face numerous
unexpected technical and engineering challenges, particularly on loss spikes
and disconvergence. In this paper, we introduce the training process of
GLM-130B including its design choices, training strategies for both efficiency
and stability, and engineering efforts. The resultant GLM-130B model offers
significant outperformance over GPT-3 175B on a wide range of popular English
benchmarks while the performance advantage is not observed in OPT-175B and
BLOOM-176B. It also consistently and significantly outperforms ERNIE TITAN 3.0
260B -- the largest Chinese language model -- across related benchmarks.
Finally, we leverage a unique scaling property of GLM-130B to reach INT4
quantization, without quantization aware training and with almost no
performance loss, making it the first among 100B-scale models. More
importantly, the property allows its effective inference on 4$\times$RTX 3090
(24G) or 8$\times$RTX 2080 Ti (11G) GPUs, the most ever affordable GPUs
required for using 100B-scale models. The GLM-130B model weights are publicly
accessible and its code, training logs, related toolkit, and lessons learned
are open-sourced at this https URL .

GLM-130B 是一种双语预训练语言模型，拥有 1300 亿个参数。通过本文我们介绍了 GLM-130B 的训练过程、设计选择、训练策略、工程努力、以及它有别于其他模型的独特的可扩展性等特点。我们观察到该模型在学习效率和稳定性方面都有非常好的表现，并能在英文和中文语料库中显著地超越 ERNIE TITAN 3.0 和 GPT-3 175B 等知名模型，且有别于其他模型的独特的可扩展性，可以在相对较低的成本下进行使用。