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 into CV-QNN (Continuous Variable Quantum Neural Networks) technology, developing 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. The CV architecture encodes information using continuous degrees of freedom, contrasting with discrete quantum bits architecture.
The system implements affine transformations through Gaussian gates and achieves nonlinearity via non-Gaussian gates. HOLO CV-QNN's potential applications include enhanced image classification, text generation, quantum chemistry, and market forecasting.
However, the technology faces challenges including quantum hardware stability, computational resource optimization, and error accumulation during network training. The company acknowledges these challenges while highlighting the technology's potential to redefine artificial intelligence capabilities as quantum hardware advances.
MicroCloud Hologram (NASDAQ: HOLO) annuncia ricerche sulla tecnologia CV-QNN (Reti Neurali Quantistiche a Variabile Continua), sviluppando Circuiti Quantistici Variazionali integrati 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 parametrizzate a strati e funzioni di attivazione non lineari per ottenere trasformazioni affini e mappature non lineari. L'architettura CV codifica le informazioni utilizzando gradi di libertà continui, in contrasto con l'architettura a bit quantistici discreti.
Il sistema implementa trasformazioni affini tramite porte gaussiane e raggiunge la non linearità attraverso porte non gaussiane. Le potenziali applicazioni di HOLO CV-QNN includono una classificazione delle immagini migliorata, generazione di testi, chimica quantistica e previsioni di mercato.
Tuttavia, la tecnologia affronta sfide come la stabilità dell'hardware quantistico, l'ottimizzazione delle risorse computazionali e l'accumulo di errori durante l'addestramento della rete. L'azienda riconosce queste sfide, evidenziando al contempo il potenziale della tecnologia di ridefinire le capacità dell'intelligenza artificiale man mano che l'hardware quantistico avanza.
MicroCloud Hologram (NASDAQ: HOLO) anuncia investigaciones sobre la tecnología CV-QNN (Redes Neuronales Cuánticas de Variable Continua), desarrollando Circuitos Cuánticos Variacionales integrados en la arquitectura CV. El objetivo de la tecnología es cuantificar las redes neuronales clásicas y diseñar modelos cuánticos especializados.
El núcleo de HOLO CV-QNN utiliza puertas cuánticas parametrizadas de forma continua en capas y funciones de activación no lineales para lograr transformaciones afines y mapeos no lineales. La arquitectura CV codifica información utilizando grados de libertad continuos, en contraste con la arquitectura de bits cuánticos discretos.
El sistema implementa transformaciones afines a través de puertas gaussianas y logra no linealidad mediante puertas no gaussianas. Las aplicaciones potenciales de HOLO CV-QNN incluyen clasificación de imágenes mejorada, generación de texto, química cuántica y pronósticos de mercado.
No obstante, la tecnología enfrenta desafíos como la estabilidad del hardware cuántico, la optimización de recursos computacionales y la acumulación de errores durante el entrenamiento de la red. La empresa reconoce estos desafíos mientras destaca el potencial de la tecnología para redefinir las capacidades de la inteligencia artificial a medida que avanza el hardware cuántico.
마이크로클라우드 홀로그램 (NASDAQ: HOLO)은 CV-QNN (연속 변수 양자 신경망) 기술에 대한 연구를 발표하며, CV 아키텍처에 통합된 변분 양자 회로를 개발하고 있습니다. 이 기술의 목표는 고전 신경망을 양자화하고 전문화된 양자 모델을 설계하는 것입니다.
HOLO CV-QNN의 핵심은 층으로 구성된 연속적으로 매개변수화된 양자 게이트와 비선형 활성화 함수를 사용하여 아핀 변환과 비선형 매핑을 달성하는 것입니다. CV 아키텍처는 정보가 연속적인 자유도를 사용하여 인코딩되며, 이는 이산 양자 비트 아키텍처와 대조됩니다.
이 시스템은 가우시안 게이트를 통해 아핀 변환을 구현하고 비가우시안 게이트를 통해 비선형성을 달성합니다. HOLO CV-QNN의 잠재적 응용 분야에는 향상된 이미지 분류, 텍스트 생성, 양자 화학 및 시장 예측이 포함됩니다.
그러나 이 기술은 양자 하드웨어 안정성, 계산 자원 최적화 및 네트워크 훈련 중 오류 누적과 같은 도전에 직면해 있습니다. 회사는 이러한 도전을 인정하면서 양자 하드웨어가 발전함에 따라 이 기술이 인공지능의 능력을 재정의할 수 있는 잠재력을 강조합니다.
MicroCloud Hologram (NASDAQ: HOLO) annonce des recherches sur la technologie CV-QNN (Réseaux Neuronaux Quantiques à Variables Continues), développant des Circuits Quantiques Variationnels intégrés dans l'architecture CV. L'objectif de la 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 et des fonctions d'activation non linéaires pour réaliser des transformations affines et des mappages non linéaires. L'architecture CV encode l'information en utilisant des degrés de liberté continus, contrairement à l'architecture des bits quantiques discrets.
Le système met en œuvre des transformations affines via des portes gaussiennes et atteint la non-linéarité grâce à des portes non gaussiennes. Les applications potentielles de HOLO CV-QNN incluent une classification d'images améliorée, la génération de textes, la chimie quantique et les prévisions de marché.
Cependant, la technologie fait face à des défis tels que la stabilité du matériel quantique, l'optimisation des ressources informatiques et l'accumulation d'erreurs durant l'entraînement du réseau. L'entreprise reconnaît ces défis tout en soulignant le potentiel de la technologie à redéfinir les capacités de l'intelligence artificielle à mesure que le matériel quantique progresse.
MicroCloud Hologram (NASDAQ: HOLO) kündigt Forschungen zur CV-QNN (Continuous Variable Quantum Neural Networks) Technologie an und entwickelt variational quantum circuits, die in die CV-Architektur eingebettet sind. Ziel der Technologie ist es, klassische neuronale Netzwerke zu quantisieren und spezialisierte quantenbasierte Modelle zu entwerfen.
Der Kern von HOLO CV-QNN nutzt geschichtete kontinuierlich parametrisierte Quantengatter und nichtlineare Aktivierungsfunktionen, um affine Transformationen und nichtlineare Abbildungen zu erreichen. Die CV-Architektur kodiert Informationen mithilfe kontinuierlicher Freiheitsgrade, im Gegensatz zur Architektur diskreter Quantenbits.
Das System implementiert affine Transformationen durch Gaußsche Gatter und erreicht Nichtlinearität über nicht-gaußsche Gatter. Zu den potenziellen Anwendungen von HOLO CV-QNN gehören verbesserte Bildklassifikation, Textgenerierung, Quantenchemie und Marktprognosen.
Die Technologie steht jedoch vor Herausforderungen wie der Stabilität der Quantenhardware, der Optimierung von Rechenressourcen und der Fehlerakkumulation während des Netzwerktrainings. Das Unternehmen erkennt diese Herausforderungen an und hebt gleichzeitig das Potenzial der Technologie hervor, die Fähigkeiten der künstlichen Intelligenz neu zu definieren, während sich die Quantenhardware weiterentwickelt.
- Technology offers potential for exponential speedup in large-scale data processing
- Strong scalability and compatibility 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 remain unresolved
- Risk of error accumulation during network training process
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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