MicroCloud Hologram Inc. Researches CV-QNN (Continuous Variable Quantum Neural Networks) Technology and Builds Variational Quantum Circuits Embedded in CV Architecture
MicroCloud Hologram (NASDAQ: HOLO) announces research developments in CV-QNN (Continuous Variable Quantum Neural Networks) technology, focusing on building Variational Quantum Circuits embedded in CV architecture. The technology aims to quantumize classical neural networks and design specialized quantum models.
The core of HOLO CV-QNN uses layered continuously parameterized quantum gates and nonlinear activation functions to achieve affine transformations and nonlinear mappings in neural networks. The CV architecture encodes information using continuous degrees of freedom, differing from discrete quantum bits (DV architecture).
The technology leverages quantum superposition and entanglement properties, offering potential exponential speedup for large-scale data processing. Applications include image classification, object detection, semantic segmentation, text generation, sentiment analysis, machine translation, quantum chemistry, and market forecasting.
However, HOLO CV-QNN faces challenges including quantum hardware stability, computational resource optimization, and error accumulation during network training.
MicroCloud Hologram (NASDAQ: HOLO) annuncia sviluppi di ricerca nella tecnologia CV-QNN (Reti Neurali Quantistiche a Variabili Continue), concentrandosi sulla costruzione di Circuiti Quantistici Variazionali incorporati nell'architettura CV. L'obiettivo della tecnologia è quello di quantizzare le reti neurali classiche e progettare modelli quantistici specializzati.
Il nucleo di HOLO CV-QNN utilizza porte quantistiche continuamente parametriche a strati e funzioni di attivazione non lineari per raggiungere trasformazioni affini e mappature non lineari nelle reti neurali. L'architettura CV codifica le informazioni utilizzando gradi di libertà continui, differente dai qubit quantistici discreti (architettura DV).
La tecnologia sfrutta le proprietà di sovrapposizione quantistica e di intreccio, offrendo un potenziale aumento esponenziale della velocità per l'elaborazione di dati su larga scala. Le applicazioni includono classificazione delle immagini, rilevamento degli oggetti, segmentazione semantica, generazione di testi, analisi del sentimento, traduzione automatica, chimica quantistica e previsione di mercato.
Tuttavia, HOLO CV-QNN affronta sfide come la stabilità dell'hardware quantistico, l'ottimizzazione delle risorse computazionali e l'accumulo di errori durante l'addestramento della rete.
MicroCloud Hologram (NASDAQ: HOLO) anuncia desarrollos de investigación en tecnología CV-QNN (Redes Neurales Cuánticas de Variables Continuas), centrándose en la construcción de Circuitos Cuánticos Variacionales integrados en la arquitectura CV. La tecnología tiene como objetivo cuantizar redes neuronales clásicas y diseñar modelos cuánticos especializados.
El núcleo de HOLO CV-QNN utiliza puertas cuánticas parametrizadas continuamente en capas y funciones de activación no lineales para lograr transformaciones afines y mapeos no lineales en redes neuronales. La arquitectura CV codifica información utilizando grados de libertad continuos, a diferencia de los bits cuánticos discretos (arquitectura DV).
La tecnología aprovecha las propiedades de superposición cuántica y entrelazamiento, ofreciendo un potencial aumento exponencial de velocidad para el procesamiento de datos a gran escala. Las aplicaciones incluyen clasificación de imágenes, detección de objetos, segmentación semántica, generación de texto, análisis de sentimientos, traducción automática, química cuántica y pronóstico de mercados.
Sin embargo, HOLO CV-QNN enfrenta desafíos que incluyen la estabilidad del hardware cuántico, la optimización de recursos computacionales y la acumulación de errores durante el entrenamiento de la red.
마이크로클라우드 홀로그램 (NASDAQ: HOLO)은 CV-QNN (연속 변수 양자 신경망) 기술의 연구 개발을 발표하며, CV 아키텍처에 내장된 변분 양자 회로 구축에 중점을 두고 있습니다. 이 기술의 목표는 고전 신경망을 양자화하고 전문화된 양자 모델을 설계하는 것입니다.
HOLO CV-QNN의 핵심은 층별로 계속 파라미터화된 양자 게이트와 비선형 활성화 함수를 사용하여 신경망에서 아핀 변환 및 비선형 매핑을 달성하는 것입니다. CV 아키텍처는 정보 encoding을 위해 연속적인 자유도를 사용하며, 이는 이산 양자 비트(DV 아키텍처)와 다릅니다.
이 기술은 양자 중첩 및 얽힘의 특성을 활용하여 대규모 데이터 처리에서 잠재적인 기하급수적 속도 향상을 제공합니다. 응용 분야로는 이미지 분류, 객체 탐지, 의미론적 분할, 텍스트 생성, 감정 분석, 기계 번역, 양자 화학 및 시장 예측이 포함됩니다.
그러나 HOLO CV-QNN은 양자 하드웨어의 안정성, 계산 자원 최적화 및 네트워크 훈련 중 오류 누적과 같은 도전 과제에 직면해 있습니다.
MicroCloud Hologram (NASDAQ: HOLO) annonce des développements de recherche dans la technologie CV-QNN (Réseaux Neuraux Quantiques à Variables Continues), en se concentrant sur la construction de Circuits Quantiques Variationnels intégrés dans l'architecture CV. L'objectif de cette technologie est de quantifier les réseaux neuronaux classiques et de concevoir des modèles quantiques spécialisés.
Le cœur de HOLO CV-QNN utilise des portes quantiques paramétrées en continu en couches et des fonctions d'activation non linéaires pour réaliser des transformations affines et des mappages non linéaires dans les réseaux neuronaux. L'architecture CV encode les informations en utilisant des degrés de liberté continus, ce qui la distingue des bits quantiques discrets (architecture DV).
La technologie exploite les propriétés de superposition quantique et d'intrication, offrant un potentiel d'accélération exponentielle pour le traitement de données à grande échelle. Les applications incluent la classification d'images, la détection d'objets, la segmentation sémantique, la génération de texte, l'analyse des sentiments, la traduction automatique, la chimie quantique et les prévisions de marché.
Cependant, HOLO CV-QNN fait face à des défis tels que la stabilité du matériel quantique, l'optimisation des ressources informatiques et l'accumulation d'erreurs lors de l'entraînement du réseau.
MicroCloud Hologram (NASDAQ: HOLO) kündigt Forschungsentwicklungen in der Technologie der CV-QNN (Continuous Variable Quantum Neural Networks) an und konzentriert sich auf den Aufbau von Variational Quantum Circuits, die in die CV-Architektur eingebettet sind. Ziel der Technologie ist es, klassische neuronale Netzwerke zu quantisieren und spezialisierte Quantmodelle zu entwerfen.
Der Kern von HOLO CV-QNN verwendet geschichtete kontinuierlich parametrisierte Quantentore und nichtlineare Aktivierungsfunktionen, um affine Transformationen und nichtlineare Zuordnungen in neuronalen Netzwerken zu erreichen. Die CV-Architektur kodiert Informationen mithilfe kontinuierlicher Freiheitsgrade, was sich von diskreten Quantenbits (DV-Architektur) unterscheidet.
Die Technologie nutzt die Eigenschaften der Quantenüberlagerung und Verschränkung und bietet ein potenzielles exponentielles Tempo für die Verarbeitung großer Datenmengen. Anwendungen umfassen Bildklassifikation, Objekterkennung, semantische Segmentierung, Textgenerierung, Sentimentanalyse, maschinelle Übersetzung, Quantenchemie und Marktprognosen.
Allerdings steht HOLO CV-QNN vor Herausforderungen wie der Stabilität der Quantenhardware, der Optimierung der Rechenressourcen und der Fehlerakkumulation während des Trainings des Netzwerks.
- Technology offers potential exponential speedup in large-scale data processing
- Strong scalability and seamless integration with existing classical computing systems
- Lower resource costs through efficient use of Gaussian and non-Gaussian gates
- Faces significant technical challenges in quantum hardware stability
- Issues with computational resource optimization still unresolved
- Potential error accumulation during training process needs addressing
SHENZHEN, China, March 17, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, they are researching CV-QNN (Continuous Variable Quantum Neural Networks) technology, with the aim of building Variational Quantum Circuits embedded in CV architecture. In this way, it is possible not only to quantumize classical neural networks, but also to design various specialized quantum models, such as convolutional quantum networks, recursive quantum networks, and residual quantum networks, providing new tools for quantum artificial intelligence technology.
The core of HOLO CV-QNN lies in achieving affine transformations and nonlinear mappings in neural networks through layered continuously parameterized quantum gates and nonlinear activation functions. The CV architecture is a form of quantum computing where information is encoded using continuous degrees of freedom, such as the amplitude and phase of electromagnetic fields. This contrasts with the DV architecture, which uses discrete quantum bits. The CV architecture is more closely aligned with classical information processing methods, thus offering inherent advantages when implementing neural networks. The basic operational units in CV architecture are Gaussian and non-Gaussian transformations of quantum states.
Affine transformations are fundamental operations in neural networks, typically composed of linear transformations (matrix multiplication) and bias terms (vector addition). In CV-QNN, affine transformations are realized through Gaussian gates. Gaussian gates are operations that preserve the Gaussian distribution of quantum states, including squeezing, displacement, and rotation gates. These gates can precisely control the amplitude and phase of quantum states, thereby simulating the linear operations in classical neural networks.
Nonlinear activation functions are key to enabling neural networks to represent complex features. In classical neural networks, common activation functions include ReLU, Sigmoid, and Tanh, among others. In the CV architecture, nonlinearity is achieved through non-Gaussian gates, such as polarized optical nonlinear operations or non-Gaussian optical crystals. The nonlinearity introduced by these non-Gaussian gates enables CV-QNN to represent more complex functions, enhancing the model’s expressiveness.
HOLOCV-QNN adopts a layered structure, with each layer composed of several continuously parameterized quantum gates. This layered design is similar to the multilayer perceptron structure in classical neural networks, allowing CV-QNN to perform complex nonlinear transformations while preserving quantum coherence. Additionally, this layered structure is theoretically universal, meaning that through appropriate combinations of gate operations, it can approximate any continuous function.
HOLO CV-QNN leverages quantum superposition and entanglement properties, offering the potential for exponential speedup when processing large-scale data. Additionally, because the information encoding in the CV architecture is closer to classical computing methods, CV-QNN boasts strong scalability and can seamlessly interface with existing classical computing systems. Moreover, the design of CV-QNN fully exploits the energy efficiency advantages of continuous-variable quantum computing. By using Gaussian and non-Gaussian gates, complex quantum operations can be achieved at a lower resource cost, providing a practical and feasible solution during the stage when quantum computer hardware is not yet fully developed.
The potential applications of CV-QNN are vast. It can achieve more efficient image classification, object detection, and semantic segmentation through quantum convolutional networks; enhance performance in text generation, sentiment analysis, and machine translation using quantum recursive networks; offer faster solutions in quantum chemistry, materials science, and complex system simulations; and enable more accurate market forecasting and risk assessment through quantum neural networks.
The emergence of HOLO Continuous Variable Quantum Neural Networks (CV-QNN) offers a fresh perspective on the integration of quantum computing and artificial intelligence. By embedding the structure and functions of classical neural networks within the framework of quantum computing, this technology not only significantly enhances the computational efficiency of models but also expands their application boundaries across different fields. From quantum convolutional networks to recursive quantum networks and residual quantum networks, CV-QNN technology demonstrates its potential in multiple scenarios, including image processing, natural language processing, and scientific computing. These advances signify that we are gradually entering an era driven by quantum artificial intelligence.
However, the HOLO CV-QNN technology still faces several challenges. For example, issues such as the stability of quantum hardware and the optimization of computational resources need further attention. Additionally, the potential accumulation of errors during the training process of quantum networks and the need for more efficient designs of quantum optimization algorithms present new challenges for both academia and industry. Nonetheless, these challenges also represent opportunities. As quantum hardware advances and software tools improve, the performance of CV-QNN will continue to enhance, and its future application scenarios will become even more widespread.
Against the backdrop of quantum technology gradually transforming the world, HOLO CV-QNN not only represents a new computational tool but also holds the potential to redefine the boundaries of artificial intelligence capabilities. It is believed that as this technology continues to develop, it will become the core driving force behind the next generation of intelligent systems. Whether it is unveiling the mysteries of nature in scientific research or solving complex practical problems in industry, the potential of CV-QNN technology will be exponentially magnified, bringing unprecedented opportunities.
About MicroCloud Hologram Inc.
MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud’s holographic technology services include high-precision holographic light detection and ranging (“LiDAR”) solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems (“ADAS”). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud’s holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud’s holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit http://ir.mcholo.com/
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