Core Insights - PacBio has licensed advanced deep learning-based DNA methylation detection methods from The Chinese University of Hong Kong (CUHK) to enhance its HiFi sequencing capabilities, specifically for detecting 5-hydroxymethylcytosine (5hmC), hemimethylated 5-methylcytosine (5mC), and N6-methyladenine (6mA) [1][2][4] Technology and Innovation - The licensed technology includes the Holistic Kinetic Model 2 (HK2), which utilizes an AI deep learning framework to improve the accuracy of methylation detection, including the ability to call native 5hmC in single molecules, a first for HiFi sequencing [2][4] - HiFi sequencing on Revio and Vega platforms allows for comprehensive genome and epigenome readouts from native DNA without chemical conversion or additional sample preparation, enhancing the efficiency of sequencing workflows [3][6] Market Impact - The integration of HK2 is expected to set a new standard for accuracy in DNA methylation detection, particularly for 5mC and 5hmC, which are crucial for research in cancer and human development [4][7] - Institutions like Children's Mercy Kansas City and GeneDx are already utilizing HiFi sequencing for genomic and epigenomic profiling, indicating a growing adoption of this technology in clinical settings [5] Future Prospects - The new capabilities from HK2 will be delivered to existing customers through software updates, ensuring no additional costs or changes to current sequencing protocols [3][8] - The ability to profile 5hmC is anticipated to open new avenues in liquid biopsy and cancer detection, maintaining DNA integrity and supporting haplotype-resolved analysis [6][8]

Core Viewpoint - WiMi Hologram Cloud Inc. has developed a Quantum Computing-Based Feedforward Neural Network (QFNN) algorithm that addresses computational bottlenecks in traditional neural network training, utilizing Quantum Random Access Memory (QRAM) for efficient data processing [1][10]. Quantum Algorithm Development - The QFNN algorithm incorporates key quantum computing subroutines, particularly in the feedforward and backpropagation processes, providing exponential speedup in both stages of neural network training [2][4]. - Classical feedforward propagation, which involves multiple matrix-vector multiplications, is enhanced by the quantum algorithm through the use of quantum state superposition and coherence, allowing computations to be performed in logarithmic time [3][6]. Computational Efficiency - The quantum algorithm significantly reduces computational complexity, shifting from a dependency on the number of connections (O(M)) in classical networks to a dependency solely on the number of neurons (O(N)) in the quantum framework [6][7]. - This reduction in complexity leads to at least a quadratic speedup in training large-scale neural networks, making it particularly advantageous for ultra-large-scale datasets [7]. Overfitting Mitigation - WiMi's quantum algorithm demonstrates inherent resilience to overfitting, a common issue in deep learning, due to the intrinsic uncertainty of quantum computing, which acts similarly to regularization techniques [8][9]. Application Prospects - The QFNN algorithm has broad application potential in fields requiring high computational speed and data scale, such as financial market analysis, autonomous driving, biomedical research, and quantum computer vision [10][11]. - Additionally, the research lays the groundwork for quantum-inspired classical algorithms that can optimize computational complexity on traditional computers, providing a transitional solution until quantum computers become widely available [10]. Future Implications - The advancement of WiMi's QFNN algorithm marks a significant milestone in the intersection of quantum computing and machine learning, suggesting that quantum neural networks will play a crucial role in the future of artificial intelligence [11][12].

实习时间： 每周至少实习3天以上，实习时间不少于3个月，不满足的请勿投递，实习考核优秀者有留用机会。 岗位职责： 1、负责数据处理、分析、统计等工作，协助研究员完成量化投资相关课题的研究； 实习生招聘 工作地点： 深圳、广州、上海、北京 ，要求线下实习 简历投递截止日期： 2025年4月30日 2、协助进行金融工程策略模型的开发与跟踪等工作； 3、完成小组安排的其他工作。 基本要求： 1、数学、统计、物理、计算机、信息工程等理工科专业，或金融工程相关专业，硕士或博士在读，特别优秀的大四 保研亦可，非应届（2026年及之后毕业）； 2、熟练掌握Python等编程语言，熟悉SQL数据库，有优秀编程能力与编程规范； 3、有责任心，自我驱动能力强， 具有良好的信息搜集能力、逻辑思维能力、分析判断能力、言语和书面表达能力、 人际沟通能力。 加分项： 4、 具备扎实的金融市场基础知识，熟悉股票、债券、期货、指数及基金等核心概念； 5、数学基础好，有科研项目经历、有学术论文被SCI或EI收录； 6、熟悉Wind、 Bloomberg、天软等金融终端； 7、熟悉机器学习、深度学习，熟悉PyTorch、Linux，有GPU服务 ...