SHENZHEN, China, June 10, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, announced the development of a noise-resistant Deep Quantum Neural Network (DQNN) architecture aimed at achieving universal quantum computing and optimizing the training efficiency of quantum learning tasks. This innovation is not merely a quantum simulation of traditional neural networks but a deep quantum learning framework capable of processing real quantum ...

Top three teams recognized for ideas to create sustainable electric mobility, tumor diagnostics accuracy and remote neurological monitoring BENGALURU, India, and BLUE BELL, Pa., June 5, 2025 /PRNewswire/ -- Unisys (NYSE: UIS) is pleased to announce the winners of the 16th iteration of the Unisys Innovation Program (UIP), a competition for engineering students across India. Established in 2009, the program bridges the gap between academic learning and hands-on experience by bringing young engineers together ...

The core of HOLO's end-to-end quantum-accelerated classifier method lies in constructing a classification problem and designing a quantum kernel learning approach that leverages quantum computing for acceleration. In this process, a carefully constructed dataset is proposed, and it is proven that, under the widely accepted assumption that the discrete logarithm problem is computationally difficult, no classical learner can classify this data with inverse polynomial accuracy better than random guessing. The ...

Hybrid Quantum-Classical Architecture: Combining the parallel advantages of quantum computing with the flexibility of classical computing to achieve efficient collaborative training. shenzhen, May 20, 2025 (GLOBE NEWSWIRE) -- Shenzhen, May. 20, 2025/––MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), announced that quantum algorithms will be deeply integrated with machine learning to explore practical application scenarios for quantum acceleration. Quantum machine learning algorithms represent a ...

文章核心观点 - 公司宣布开发基于量子纠缠的监督量子分类器训练算法，突破传统算法能力限制，虽量子计算面临挑战但该技术在机器学习领域潜力大 [1][12] 算法介绍 - 开发新型基于量子纠缠的监督量子分类器训练算法，引入基于贝尔不等式的成本函数，可同时编码多个训练样本误差 [1] - 算法核心是利用量子纠缠构建能同时处理多个训练样本及其标签的模型，可并行处理多个样本，提升训练效率 [2] - 用量子叠加将训练样本表示为量子比特向量，通过量子门操作将标签信息编码到量子态，利用纠缠关系同时处理多个样本 [3] - 基于贝尔不等式的成本函数可同时编码多个样本分类误差，优化过程考虑多个样本集体性能，克服传统算法局部优化问题 [4] 算法实现 - 依赖量子计算技术的量子比特、量子门操作和量子测量等核心组件处理输入数据 [5] - 算法初始阶段将输入训练样本转换为量子比特并初始化为特定量子态，对多个量子比特进行纠缠操作 [6] - 训练样本被安排成纠缠态，通过纠缠共享和处理信息，提高数据处理效率并加速训练收敛 [7] - 利用贝尔不等式构建成本函数以最小化分类误差，通过量子算法计算有效最小化成本函数 [8] - 算法通过量子测量输出分类结果，量子计算并行处理能力可在短时间内完成复杂分类任务 [8] 技术优势 - 利用量子纠缠特性并行处理多个训练样本，加速训练速度并提高分类准确率，可克服传统方法处理大数据集的计算瓶颈 [9] - 基于贝尔不等式的成本函数理论上更稳健，可避免传统方法的局部最优问题，在复杂分类任务中更有效 [10] 面临挑战 - 量子计算面临稳定性和计算规模等限制，量子比特数量和误差率影响算法实际性能，在现有平台实现高效算法需突破技术障碍 [11] 公司概况 - 公司致力于定制中央处理算法的开发和应用，通过将算法与软硬件结合为客户提供综合解决方案 [13] - 服务包括算法优化、加速计算能力、轻量级数据处理和数据智能服务等，高效交付软硬件优化是长期发展动力 [13]

SHENZHEN, China, May 2, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO) announced today the launch of their latest classifier auto-optimization technology based on Variational Quantum Algorithms (VQA). This technology significantly reduces the complexity of parameter updates during training through deep optimization of the core circuit, markedly improving computational efficiency. Compared to other quantum classifiers, this optimized model has lower complexity and incorpora ...

SHENZHEN, China, May 1, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), announced today that their newly developed quantum edge detection algorithm has broken through the limitations of classical methods. This technology optimizes the feature extraction process through quantum circuits, reducing computational complexity from O(N²) to O(N) while maintaining detection accuracy, thereby providing new solutions for real-time image processing and edge intelligence devices.The q ...