WiMi Lays Out Variational Quantum Algorithms for Multidimensional Data Task Processing
WiMi Hologram Cloud (NASDAQ: WIMI) has announced research advances in multidimensional pooling optimization using variational quantum algorithms. The company has developed a novel approach combining Quantum Haar Transform (QHT) and quantum partial measurement for processing complex multidimensional data.
The technology leverages quantum computing properties like superposition and entanglement to efficiently transform and process multidimensional data while preserving key features. This advancement allows for more effective handling of various data types, from one-dimensional audio to three-dimensional hyperspectral data, potentially improving quantum machine learning models.
WiMi Hologram Cloud (NASDAQ: WIMI) ha annunciato progressi di ricerca nell'ottimizzazione di pooling multidimensionale utilizzando algoritmi quantistici variationali. L'azienda ha sviluppato un nuovo approccio che combina Quantum Haar Transform (QHT) e misurazione quantistica parziale per l'elaborazione di dati multidimensionali complessi.
La tecnologia sfrutta proprietà del calcolo quantistico come sovrapposizione e intreccio per trasformare ed elaborare efficacemente dati multidimensionali conservando caratteristiche chiave. Questo avanzamento permette una gestione più efficace di diversi tipi di dati, dall'audio unidimensionale ai dati iperspettrali tridimensionali, potenzialmente migliorando i modelli di machine learning quantistico.
WiMi Hologram Cloud (NASDAQ: WIMI) ha anunciado avances de investigación en la optimización de pooling multidimensional utilizando algoritmos cuánticos variacionales. La empresa ha desarrollado un enfoque innovador que combina Quantum Haar Transform (QHT) y medición cuántica parcial para el procesamiento de datos multidimensionales complejos.
La tecnología aprovecha propiedades de la computación cuántica como superposición y entrelazamiento para transformar y procesar de manera eficiente datos multidimensionales, preservando características clave. Este avance permite un manejo más eficaz de diversos tipos de datos, desde audio unidimensional hasta datos hiperespectrales tridimensionales, potencialmente mejorando los modelos de aprendizaje automático cuántico.
WiMi Hologram Cloud (NASDAQ: WIMI)은 다차원 풀링 최적화를 위한 변분 양자 알고리즘 연구 진전을 발표했다. 회사는 Quantum Haar Transform (QHT)와 양자 부분 측정을 결합한 새로운 접근 방식을 개발하여 복잡한 다차원 데이터를 처리한다.
이 기술은 중첩과 얽힘과 같은 양자 컴퓨팅 특성을 활용하여 핵심 특징을 유지하면서 다차원 데이터를 효율적으로 변환하고 처리한다. 이 발전은 1차원 오디오에서 3차원 하이퍼스펙트럴 데이터에 이르기까지 다양한 데이터 유형을 더 효과적으로 다룰 수 있게 하여 양자 기계 학습 모델의 성능 향상을 가능하게 한다.
WiMi Hologram Cloud (NASDAQ: WIMI) a annoncé des avancées de recherche dans l’optimisation de l’échantillonnage multidimensionnel utilisant des algorithmes quantiques variationnels. L’entreprise a développé une approche novatrice combinant Quantum Haar Transform (QHT) et mesure partielle quantique pour le traitement de données multidimensionnelles complexes.
La technologie exploite des propriétés de l’informatique quantique telles que la superposition et l’intrication pour transformer et traiter efficacement des données multidimensionnelles tout en préservant les caractéristiques clés. Cette avancée permet une gestion plus efficace de divers types de données, allant de l’audio unidimensionnel aux données hyperspectrales tridimensionnelles, ce qui pourrait améliorer les modèles d’apprentissage automatique quantique.
WiMi Hologram Cloud (NASDAQ: WIMI) hat Forschungsfortschritte in der multidimensionalen Pooling-Optimierung mittels variationalen Quantenalgorithmen bekannt gegeben. Das Unternehmen hat einen neuartigen Ansatz entwickelt, der Quantum Haar Transform (QHT) und quantenbasierte Teilmessung zur Verarbeitung komplexer multidimensionaler Daten kombiniert.
Die Technologie nutzt Eigenschaften der Quantenberechnung wie Superposition und Verschränkung, um multidimensionale Daten effizient zu transformieren und zu verarbeiten und gleichzeitig wichtige Merkmale zu erhalten. Dieser Fortschritt ermöglicht eine effektivere Handhabung verschiedener Datentypen, von eindimensionalem Audio bis zu dreidimensionalen Hyperspektraldaten, und könnte Modelle des quantum Maschinenlernens verbessern.
WiMi Hologram Cloud (NASD AQ: WIMI) أعلنت عن تقدم في البحث في تحسين التجميع متعدد الأبعاد باستخدام خوارزميات كمومية مت variational. تعم الشركة نهجاً جديداً يجمع بين Quantum Haar Transform (QHT) وقياس كمي جزئي لمعالجة البيانات متعددة الأبعاد المعقدة.
تستفيد التكنولوجيا من خصائص الحوسبة الكمية مثل التراكب والتشابك لمعالجة وتحويل البيانات متعددة الأبعاد بكفاءة مع الحفاظ على السمات الرئيسية. هذا التقدم يسمح بمعالجة أكثر فاعلية لمجموعة أنواع البيانات المختلفة، من الصوت أحادي البعد إلى البيانات الطيفية الفوقية ثلاثية الأبعاد، مما قد يحسن نماذج التعلم الآلي الكمي.
WiMi Hologram Cloud (NASDAQ: WIMI) 宣布在使用 变分量子算法 的多维 Poolling 优化 方面取得研究进展。公司开发了一种将 Quantum Haar Transform (QHT) 与 量子部分测量 相结合的新方法，用于处理复杂的多维数据。
该技术利用量子计算的特性，如 叠加和纠缠，在保持关键特征的同时高效地变换和处理多维数据。这一进展使得处理从一维音频到三维高光谱数据等各种数据类型更加高效，可能提升 量子机器学习模型。
- None.
- Technology is still in research phase with no immediate commercialization timeline
- Success depends on future quantum hardware improvements
- No concrete financial or business impact metrics provided
Through QHT, multidimensional data is mapped to a quantum state space, where each qubit represents a dimension or feature of the data. This mapping not only preserves the global structure of the data but also enhances the expression of local features. After the Quantum Haar Transform, quantum partial measurement techniques can selectively extract key information from the quantum state, enabling the pooling operation for multidimensional data. Unlike traditional pooling methods that directly discard part of the data, quantum partial measurement leverages the probabilistic nature of quantum states to retain the most important feature information in probabilistic form, according to predefined pooling strategies (such as max pooling, average pooling, etc.). This process not only reduces the data dimensionality but also preserves the locality and key features of the data, providing high-quality input for subsequent quantum classification or regression tasks.
Variational Quantum Algorithms (VQA) are hybrid algorithms that combine quantum computing and classical optimization. By using parameterized quantum circuits and optimization techniques such as gradient descent, VQAs iteratively adjust quantum states to minimize a given loss function. In multidimensional pooling optimization, VQA is used to optimize parameters, ensuring that the pooling operation can accurately capture key features of the data while maintaining computational efficiency and accuracy. Through an iterative optimization process, VQA continually adjusts the parameters of the quantum circuit so that the quantum state transformation and measurement process can maximally preserve the locality and feature structure of the data. Moreover, VQAs can directly perform pooling operations on multidimensional data without the need to reduce the data to one dimension, effectively retaining the locality and structural information of the data. The superposition and entanglement properties of quantum states enable more rich representations of multidimensional data in quantum space, helping to extract finer and more complex features. The utilization of quantum parallelism and entanglement allows VQA to significantly accelerate computation when handling large-scale multidimensional data, improving the efficiency of model training and inference. The VQA framework is highly scalable and can accommodate various types of multidimensional data processing needs, ranging from one-dimensional audio data to two-dimensional image data and even three-dimensional hyperspectral data. By adjusting the parameters and structure of the quantum circuit, VQA can be flexibly applied to different dimensional data processing tasks.
The multidimensional pooling optimization technology under the Variational Quantum Algorithm framework researched by WiMi provides a new solution for quantum machine learning in handling complex multidimensional data tasks. It not only overcomes the limitations of traditional pooling methods when dealing with high-dimensional data but also fully leverages the unique advantages of quantum computing. As quantum computing technology continues to develop and mature, the multidimensional pooling optimization technology under the VQA framework is expected to demonstrate its enormous application potential and value in more fields. In the future, with improvements in quantum hardware and algorithm optimization, this technology is expected to provide strong support for building more efficient and accurate quantum machine learning models.
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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SOURCE WiMi Hologram Cloud Inc.
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