Stochastic computing offers a probabilistic approach to address challenges
posed by problems with uncertainty and noise in various fields, particularly
machine learning. The realization of stochastic computing, however, faces the
limitation of developing reliable stochastic logics. Here, we present
stochastic logics development using memristors. Specifically, we integrate
memristors into logic circuits to design the stochastic logics, wherein the
inherent stochasticity in memristor switching is harnessed to enable stochastic
number encoding and processing with well-regulated probabilities and
correlations. As a practical application of the stochastic logics, we design a
compact stochastic Roberts cross operator for edge detection. Remarkably, the
operator demonstrates exceptional contour and texture extractions, even in the
presence of 50% noise, and owning to the probabilistic nature and compact
design, the operator can consume 95% less computational costs required by
conventional binary computing. The results underscore the great potential of
our stochastic computing approach as a lightweight local solution to machine
learning challenges in autonomous driving, virtual reality, medical diagnosis,
industrial automation, and beyond.

通过将忆阻器集成到逻辑电路中，我们开发了一种随机逻辑的方法，其中利用了忆阻器开关的内在随机性来实现具有良好概率和相关性的随机数编码和处理。作为一种实际应用，我们设计了一个紧凑的随机 Roberts 交叉算子用于边缘检测，该算子在 50％噪声存在的情况下展现了出色的轮廓和纹理提取性能，并且由于概率性质和紧凑设计，该算子所需的计算成本比传统二进制计算低 95％。这些结果突显了我们的随机计算方法在自动驾驶、虚拟现实、医学诊断、工业自动化等领域中作为轻量级本地解决方案的巨大潜力。

基于 memristor 启用的随机逻辑的本地随机计算

Local stochastic computing using memristor-enabled stochastic logics

The research explores the potential of quantum deep learning models to
address challenging machine learning problems that classical deep learning
models find difficult to tackle. We introduce a novel model architecture that
combines classical convolutional layers with a quantum neural network, aiming
to surpass state-of-the-art accuracy while maintaining a compact model size.
The experiment is to classify high-dimensional audio data from the Bird-CLEF
2021 dataset. Our evaluation focuses on key metrics, including training
duration, model accuracy, and total model size. This research demonstrates the
promising potential of quantum machine learning in enhancing machine learning
tasks and solving practical machine learning challenges available today.

该研究探索了量子深度学习模型在解决传统深度学习模型难以处理的具有挑战性的机器学习问题方面的潜力。我们介绍了一种新型模型架构，将经典卷积层与量子神经网络相结合，旨在超越现有最先进的准确性，同时保持紧凑的模型尺寸。该实验对来自 Bird-CLEF 2021 数据集的高维音频数据进行分类，我们的评估重点关注训练持续时间、模型准确性和总模型尺寸等关键指标。这项研究证明了量子机器学习在增强机器学习任务和解决当今实际机器学习挑战方面的潜力。