WiMi Lays Out Scalable Quantum Convolutional Neural Network to Enhance Image Classification Accuracy and Efficiency
WiMi Hologram Cloud (NASDAQ: WIMI) has announced its exploration of Scalable Quantum Convolutional Neural Networks (SQCNN) technology to enhance image classification capabilities. The company's new SQCNN model demonstrates superior performance over traditional quantum neural networks through optimized qubit utilization and unique network architecture.
The technology leverages parallel processing across multiple quantum devices, allowing simultaneous feature extraction from different parts of an image. This innovative approach enables dynamic adaptation to various task scales, making it suitable for applications in autonomous driving and medical image analysis. The system's key advantages include improved classification accuracy, better generalization capabilities, and enhanced training efficiency through quantum computing architectures.
WiMi Hologram Cloud (NASDAQ: WIMI) ha annunciato la propria esplorazione della tecnologia Scalable Quantum Convolutional Neural Networks (SQCNN) per migliorare le capacità di classificazione delle immagini. Il nuovo modello SQCNN dell'azienda dimostra una prestazione superiore rispetto alle tradizionali reti neurali quantistiche grazie all'ottimizzazione dell'uso dei qubit e a un'architettura di rete unica.
La tecnologia sfrutta l'elaborazione in parallelo su più dispositivi quantistici, consentendo l'estrazione di caratteristiche simultanea da diverse parti di un'immagine. Questo approccio innovativo consente un adattamento dinamico a diverse scale di compito, rendendolo adatto ad applicazioni in guida autonoma e analisi di immagini mediche. I principali vantaggi del sistema includono una maggiore accuratezza di classificazione, migliori capacità di generalizzazione e una maggiore efficienza di addestramento attraverso architetture di calcolo quantistico.
WiMi Hologram Cloud (NASDAQ: WIMI) ha anunciado su exploración de la tecnología de Redes Neuronales Convolucionales Cuánticas Escalables (SQCNN) para mejorar las capacidades de clasificación de imágenes. El nuevo modelo SQCNN de la empresa demuestra un rendimiento superior frente a las redes neuronales cuánticas tradicionales gracias a una optimización del uso de qubits y a una arquitectura de red única.
La tecnología aprovecha el procesamiento paralelo en múltiples dispositivos cuánticos, permitiendo la extracción de características simultánea desde diferentes partes de una imagen. Este enfoque innovador permite una adaptación dinámica a varias escalas de tarea, haciéndolo apto para aplicaciones en conducción autónoma y análisis de imágenes médicas. Las principales ventajas del sistema incluyen mayor precisión de clasificación, mejores capacidades de generalización y mayor eficiencia de entrenamiento mediante arquitecturas de computación cuántica.
WiMi Hologram Cloud (NASDAQ: WIMI) 는 이미지 분류 능력을 향상시키기 위해 확장 가능한 양자 합성 신경망(SQCNN) 기술의 탐구를 발표했습니다. 회사의 새로운 SQCNN 모델은 전통적인 양자 신경망에 비해 우수한 성능을 보여주며, 큐빗의 활용 최적화와 독특한 네트워크 아키텍처를 통해 달성됩니다.
이 기술은 다수의 양자 소자에 걸친 병렬 처리를 활용하여 이미지의 서로 다른 부분에서 특징을 동시에 추출합니다. 이 혁신적인 접근 방식은 다양한 작업 규모에 동적으로 적응할 수 있게 하여 자율 주행 및 의학 영상 분석과 같은 응용 분야에 적합합니다. 시스템의 주요 이점으로는 분류 정확도 향상, 더 나은 일반화 능력, 양자 컴퓨팅 아키텍처를 통한 향상된 학습 효율성이 포함됩니다.
WiMi Hologram Cloud (NASDAQ: WIMI) a annoncé son exploration de la technologie des Réseaux Neuronaux Convolutionnels Quantiques Évolutifs (SQCNN) afin d’améliorer les capacités de classification d’images. Le nouveau modèle SQCNN de l’entreprise démontre une performance supérieure par rapport aux réseaux neuronaux quantiques traditionnels grâce à l’optimisation de l’utilisation des qubits et à une architecture réseau unique.
La technologie exploite le traitement parallèle sur plusieurs dispositifs quantiques, permettant l’extraction simultanée de caractéristiques à partir de différentes parties d’une image. Cette approche innovante permet une adaptation dynamique à différentes échelles de tâche, ce qui la rend adaptée aux applications dans la conduite autonome et l’analyse d’images médicales. Les principaux avantages du système incluent une précision de classification accrue, de meilleures capacités de généralisation et une efficacité d’entraînement améliorée grâce à des architectures de calcul quantique.
WiMi Hologram Cloud (NASDAQ: WIMI) hat seine Erkundung der Technologie der Skalierbaren Quanten-Kovolutionellen Neuronalen Netze (SQCNN) angekündigt, um die Bildklassifikationsfähigkeiten zu verbessern. Das neue SQCNN-Modell des Unternehmens zeigt eine überlegene Leistung gegenüber traditionellen Quanten-Neuronalen Netzen, dank optimierter Qubit-Nutzung und einer einzigartigen Netzwerkarchitektur.
Die Technologie nutzt parallele Verarbeitung auf mehreren Quanten-Geräten, wodurch eine gleichzeitige Merkmalsextraktion aus verschiedenen Bildteilen ermöglicht wird. Dieser innovative Ansatz ermöglicht eine dynamische Anpassung an verschiedene Aufgaben-Skalen und eignet sich damit für Anwendungen in autonomem Fahren und medizinischer Bildanalyse. Zu den Hauptvorteilen des Systems gehören eine verbesserte Klassifikationsgenauigkeit, bessere Generalisierungspotenziale und eine verbesserte Trainings-Effizienz durch Quantencomputing-Architekturen.
WiMi Hologram Cloud (NASDAQ: WIMI) أعلنت عن استكشافها لتقنية شبكات الأعصاب التلافيفية الكمية القابلة للتوسع (SQCNN) من أجل تعزيز قدرات تصنيف الصور. يبرز النموذج الجديد SQCNN الخاص بالشركة أداءً فائقاً مقارنةً بالشبكات العصبية الكمية التقليدية من خلال تحسين استخدام الكيوبت وعمارة الشبكة الفريدة.
تستفيد التقنية من المعالجة المتوازية عبر عدة أجهزة كمومية، مما يسمح باستخراج الميزات بشكل متزامن من أجزاء مختلفة من الصورة. هذا النهج المبتكر يتيح التكيف الديناميكي مع مقاييس مهام مختلفة، مما يجعله مناسباً لتطبيقات في القيادة الذاتية وتحليل الصور الطبية. تشمل المزايا الرئيسية للنظام دقة تصنيف محسنة، قدرات تعميم أفضل، وكفاءة تدريب محسنة من خلال تقنيات الحوسبة الكمية.
WiMi Hologram Cloud (NASDAQ: WIMI) 已宣布开始探索可扩展量子卷积神经网络（SQCNN）技术，以提升图像分类能力。该公司新的 SQCNN 模型通过优化量子比特使用和独特的网络架构，在<传统量子神经网络上显示出更高的性能。
该技术利用< b>在多台量子设备上的并行处理，实现对图像不同部分的特征同时提取。这种创新方法使其能够动态适应各种任务规模，适用于自动驾驶和医疗图像分析等应用。系统的主要优势包括提高的分类准确性、 更好的泛化能力，以及通过量子计算架构实现的训练效率提升。
- Advanced SQCNN technology shows superior performance over traditional quantum neural networks
- Parallel processing capability enables faster and more efficient image feature extraction
- Flexible scalability allows cost optimization for different task complexities
- Technology applicable to high-value markets including autonomous driving and medical imaging
- No concrete implementation timeline or commercial deployment plans provided
- Early-stage technology with unproven market viability
- Potential high implementation costs for quantum computing infrastructure
Traditional quantum neural network models, when handling complex image classification tasks, often suffer from biases in classification results due to incomplete or inaccurate feature extraction. In contrast, the scalable quantum convolutional neural network model, through optimized utilization of qubits and a unique network architecture design, can more accurately extract key features from images, thereby significantly improving classification accuracy. Additionally, in terms of model generalization, the scalable quantum convolutional neural network model can better adapt to the characteristics of different datasets, enabling accurate classification even when faced with new data. This advantage makes it more stable and reliable in practical applications, preventing significant performance degradation due to minor data variations. In terms of training efficiency, the scalable quantum convolutional neural network model greatly reduces the time required for training through optimization of quantum algorithms. By leveraging advanced algorithms and efficient quantum computing architectures, the scalable quantum convolutional neural network model significantly enhances application efficiency.
In traditional convolutional neural networks, the convolutional layer performs convolution operations on the image through a sliding convolution kernel to extract local features of the image. In the quantum circuit of the scalable quantum convolutional neural network, similar functionality is achieved by relying on the superposition and entanglement properties of quantum gates. The superposition of quantum gates allows qubits to exist in multiple states simultaneously, which is equivalent to processing multiple features at the same time, significantly improving processing efficiency. The entanglement between qubits establishes more complex correlations, enabling the quantum circuit to learn subtler and deeper features in the image. This unique design allows the quantum circuit of the scalable quantum convolutional neural network to better learn features, providing a solid foundation for subsequent classification tasks.
In particular, in the scalable quantum convolutional neural network system, multiple independent quantum devices can extract features in parallel, a design that is highly innovative and practical. In traditional machine learning tasks, feature extraction is often performed sequentially, which limits processing speed and efficiency. In contrast, the parallel design in the scalable quantum convolutional neural network system allows different quantum devices to simultaneously extract features from different parts of an image or different types of features, akin to multiple workers operating simultaneously in different areas, significantly accelerating the speed of feature extraction. Moreover, this design allows for the flexible use of quantum devices of varying sizes. When facing machine learning tasks of different scales and complexities, quantum devices of appropriate sizes can be selected and combined based on actual needs. For simple, small-scale tasks, smaller quantum devices can be used to reduce costs and computational complexity; for complex, large-scale tasks, multiple larger-scale quantum devices can be combined to meet the computational demands of the task, thereby enabling larger-scale machine learning tasks.
The scalable quantum convolutional neural network explored by WiMi not only achieves parallelization and multidimensionality in feature extraction but also breaks the conflict between computational resources and task complexity through its ability to dynamically adapt to the scale of quantum devices. This innovation not only significantly enhances the accuracy and efficiency of image classification but also strikes a balance between generalization capability and training costs, providing technical support for high-real-time, high-complexity scenarios such as autonomous driving and medical image analysis. With the continuous development of quantum technology, it will propel artificial intelligence toward a higher-dimensional computational paradigm.
About WiMi Hologram Cloud
WiMi Hologram Cloud, Inc. (NASDAQ:WiMi) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
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