MicroAlgo Inc. Researches Quantum Machine Learning Algorithms to Accelerate Machine Learning Tasks
MicroAlgo Inc. (NASDAQ: MLGO) announced its research into integrating quantum algorithms with machine learning for quantum acceleration applications. The company is developing quantum machine learning technology through a closed-loop process involving problem modeling, quantum circuit design, experimental validation, and optimization iteration.
The technology leverages quantum bits' properties like superposition and entanglement for parallel data processing, focusing on key areas including quantum feature mapping, circuit optimization, hybrid quantum-classical architecture, and noise suppression techniques. MicroAlgo's approach aims to process complex datasets faster while improving model training speed and prediction accuracy.
The company identifies potential applications across multiple sectors including financial services (time-series analysis), healthcare (personalized treatment plans), logistics (supply chain optimization), cybersecurity, smart manufacturing, and energy management.
MicroAlgo Inc. (NASDAQ: MLGO) ha annunciato le sue ricerche sull'integrazione di algoritmi quantistici con il machine learning per applicazioni di accelerazione quantistica. L'azienda sta sviluppando una tecnologia di quantum machine learning attraverso un processo a ciclo chiuso che comprende la modellazione del problema, la progettazione di circuiti quantistici, la validazione sperimentale e l'iterazione di ottimizzazione.
La tecnologia sfrutta le proprietà dei qubit, come la sovrapposizione e l'entanglement, per l'elaborazione parallela dei dati, concentrandosi su aree chiave quali la mappatura delle caratteristiche quantistiche, l'ottimizzazione dei circuiti, l'architettura ibrida quantistica-classica e le tecniche di soppressione del rumore. L'approccio di MicroAlgo mira a elaborare dataset complessi più rapidamente, migliorando la velocità di addestramento dei modelli e l'accuratezza delle previsioni.
L'azienda individua potenziali applicazioni in diversi settori, tra cui i servizi finanziari (analisi delle serie temporali), la sanità (piani di trattamento personalizzati), la logistica (ottimizzazione della catena di approvvigionamento), la cybersecurity, la produzione intelligente e la gestione dell'energia.
MicroAlgo Inc. (NASDAQ: MLGO) anunció su investigación sobre la integración de algoritmos cuánticos con aprendizaje automático para aplicaciones de aceleración cuántica. La empresa está desarrollando tecnología de aprendizaje automático cuántico mediante un proceso de ciclo cerrado que incluye modelado de problemas, diseño de circuitos cuánticos, validación experimental e iteración de optimización.
La tecnología aprovecha las propiedades de los qubits, como la superposición y el entrelazamiento, para el procesamiento paralelo de datos, enfocándose en áreas clave como el mapeo de características cuánticas, la optimización de circuitos, la arquitectura híbrida cuántico-clásica y técnicas de supresión de ruido. El enfoque de MicroAlgo busca procesar conjuntos de datos complejos más rápido, mejorando la velocidad de entrenamiento del modelo y la precisión de las predicciones.
La compañía identifica aplicaciones potenciales en múltiples sectores, incluyendo servicios financieros (análisis de series temporales), salud (planes de tratamiento personalizados), logística (optimización de la cadena de suministro), ciberseguridad, manufactura inteligente y gestión energética.
MicroAlgo Inc. (NASDAQ: MLGO)는 양자 가속 응용을 위한 양자 알고리즘과 머신러닝 통합 연구를 발표했습니다. 회사는 문제 모델링, 양자 회로 설계, 실험 검증, 최적화 반복을 포함하는 폐쇄 루프 과정을 통해 양자 머신러닝 기술을 개발하고 있습니다.
이 기술은 중첩과 얽힘 같은 양자 비트의 특성을 활용하여 병렬 데이터 처리를 수행하며, 양자 특징 매핑, 회로 최적화, 하이브리드 양자-고전 아키텍처, 잡음 억제 기술 등 핵심 분야에 집중하고 있습니다. MicroAlgo의 접근법은 복잡한 데이터셋을 더 빠르게 처리하면서 모델 학습 속도와 예측 정확도를 향상시키는 것을 목표로 합니다.
회사는 금융 서비스 (시계열 분석), 헬스케어 (맞춤형 치료 계획), 물류 (공급망 최적화), 사이버 보안, 스마트 제조, 에너지 관리 등 여러 분야에서 잠재적 응용 가능성을 확인하고 있습니다.
MicroAlgo Inc. (NASDAQ: MLGO) a annoncé ses recherches sur l'intégration des algorithmes quantiques avec l'apprentissage automatique pour des applications d'accélération quantique. L'entreprise développe une technologie d'apprentissage automatique quantique via un processus en boucle fermée impliquant la modélisation des problèmes, la conception de circuits quantiques, la validation expérimentale et l'itération d'optimisation.
La technologie exploite les propriétés des qubits telles que la superposition et l'intrication pour le traitement parallèle des données, en se concentrant sur des domaines clés comme le mappage des caractéristiques quantiques, l'optimisation des circuits, l'architecture hybride quantique-classique et les techniques de suppression du bruit. L'approche de MicroAlgo vise à traiter des ensembles de données complexes plus rapidement tout en améliorant la vitesse d'entraînement des modèles et la précision des prédictions.
L'entreprise identifie des applications potentielles dans plusieurs secteurs, notamment les services financiers (analyse des séries temporelles), la santé (plans de traitement personnalisés), la logistique (optimisation de la chaîne d'approvisionnement), la cybersécurité, la fabrication intelligente et la gestion de l'énergie.
MicroAlgo Inc. (NASDAQ: MLGO) hat seine Forschung zur Integration von Quantenalgorithmen mit maschinellem Lernen für Anwendungen der Quantenbeschleunigung bekannt gegeben. Das Unternehmen entwickelt Quant maschinelles Lernen-Technologie durch einen geschlossenen Prozess, der Problemmodellierung, Quantenschaltkreis-Design, experimentelle Validierung und Optimierungsiteration umfasst.
Die Technologie nutzt die Eigenschaften von Qubits wie Superposition und Verschränkung für parallele Datenverarbeitung und konzentriert sich auf Schlüsselbereiche wie Quanten-Feature-Mapping, Schaltkreisoptimierung, hybride Quanten-klassische Architektur und Rauschunterdrückungstechniken. MicroAlgos Ansatz zielt darauf ab, komplexe Datensätze schneller zu verarbeiten und gleichzeitig die Modelltrainingsgeschwindigkeit und Vorhersagegenauigkeit zu verbessern.
Das Unternehmen sieht potenzielle Anwendungen in verschiedenen Branchen, darunter Finanzdienstleistungen (Zeitreihenanalyse), Gesundheitswesen (personalisierte Behandlungspläne), Logistik (Optimierung der Lieferkette), Cybersicherheit, intelligente Fertigung und Energiemanagement.
- Development of advanced quantum machine learning algorithms that can handle high-dimensional data more efficiently
- Potential applications across multiple high-value sectors (finance, healthcare, logistics)
- Implementation of hybrid quantum-classical architecture for efficient collaborative training
- Integration of noise suppression techniques and error correction to improve computational accuracy
- Research is still in early stages with no concrete implementation timeline
- Technology depends on advancement of quantum computing hardware
- No specific financial metrics or business impact details provided
- Faces technical challenges related to quantum hardware noise and error rates
shenzhen, May 20, 2025 (GLOBE NEWSWIRE) -- Shenzhen, May. 20, 2025/––MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), announced that quantum algorithms will be deeply integrated with machine learning to explore practical application scenarios for quantum acceleration.
Quantum machine learning algorithms represent an innovative approach that applies the principles of quantum computing to the field of machine learning. By leveraging the unique properties of quantum bits, such as superposition and entanglement, these algorithms enable parallel data processing and efficient computation. Compared to classical algorithms, quantum machine learning demonstrates significant advantages in feature extraction, model training, and predictive inference. It is particularly well-suited for handling high-dimensional data, optimizing combinatorial problems, and solving large-scale linear equations. Quantum machine learning algorithms can process more complex datasets in a shorter time, enhancing both the speed of model training and the accuracy of predictions.
MicroAlgo's development of quantum machine learning technology follows a closed-loop process of "problem modeling - quantum circuit design - experimental validation - optimization iteration." For specific machine learning tasks (such as classification, regression, or clustering), the team preprocesses classical data into quantum state inputs, mapping feature vectors into a quantum system using techniques like amplitude encoding or density matrix encoding. Quantum circuits are designed based on task requirements, for instance, by employing variational quantum algorithms (VQA) to construct trainable parameterized quantum gate sequences, with a classical optimizer adjusting the quantum circuit parameters to minimize the target function. During the quantum computing execution phase, the circuits are run on a quantum computer or cloud platform, and quantum measurement results are obtained and converted into classical data outputs.Validate model performance through classical post-processing, analyze error sources, and reverse optimize quantum circuit structure and parameters.
Quantum Feature Mapping: Embedding classical data into a quantum state space, enhancing data distinguishability through techniques such as quantum Fourier transform or amplitude amplification.
Quantum Circuit Optimization: Employing adaptive variational algorithms to dynamically adjust circuit depth, balancing computational resources with model expressiveness.
Hybrid Quantum-Classical Architecture: Combining the parallel advantages of quantum computing with the flexibility of classical computing to achieve efficient collaborative training.
Noise Suppression Techniques: Addressing the noise issues in current quantum hardware by introducing quantum error correction codes and error mitigation strategies to improve computational accuracy.
MicroAlgo's quantum machine learning algorithms leverage the parallelism and efficiency of quantum computing to accelerate the execution of machine learning tasks, enabling the processing of more complex datasets in shorter timeframes while improving model training speed and prediction accuracy. These quantum machine learning algorithms can handle high-dimensional data and complex patterns that traditional machine learning algorithms struggle to address. The unique properties of quantum bits, such as superposition and entanglement, allow quantum machine learning algorithms to efficiently represent and process data in high-dimensional spaces, uncovering complex patterns that conventional algorithms cannot capture. Additionally, MicroAlgo's quantum machine learning algorithms offer strong scalability and flexibility, making them adaptable to datasets of varying sizes and types as well as diverse machine learning task requirements.
The quantum machine learning algorithms researched by MicroAlgo hold broad application prospects across multiple domains. In the financial sector, these algorithms can be used for predicting and analyzing financial time-series data, enhancing the accuracy and efficiency of trading decisions. In the medical field, quantum machine learning algorithms can support the development and implementation of personalized healthcare plans by analyzing patients’ genetic information and clinical data, accurately predicting treatment outcomes and providing tailored medical solutions. In the logistics sector, these algorithms can be applied to supply chain management and logistics optimization tasks, offering analytical and decision-making support to help businesses improve operational efficiency and reduce costs. Furthermore, quantum machine learning algorithms can also be utilized in areas such as cybersecurity, smart manufacturing, and energy management, delivering efficient data analysis and optimization solutions for these fields.
As quantum computing technology continues to advance and research into quantum machine learning algorithms deepens, quantum algorithms are poised to address challenges that classical computers cannot solve, bringing disruptive innovations to various industries in the future.
About MicroAlgo Inc.
MicroAlgo Inc. (the “MicroAlgo”), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo's services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo's ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo's long-term development.
Forward-Looking Statements
This press release contains statements that may constitute "forward-looking statements." Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo's periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC's website, www.sec.gov. Words such as "expect," "estimate," "project," "budget," "forecast," "anticipate," "intend," "plan," "may," "will," "could," "should," "believes," "predicts," "potential," "continue," and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo's expectations with respect to future performance and anticipated financial impacts of the business transaction.
MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law.
Contact
MicroAlgo Inc.
Investor Relations
Email: ir@microalgor.com
FAQ
What is MicroAlgo's (MLGO) approach to quantum machine learning?
What are the key features of MLGO's quantum machine learning technology?
Which industries could benefit from MicroAlgo's quantum machine learning algorithms?
How does MicroAlgo (MLGO) address quantum computing noise issues?
What advantages does MLGO's quantum machine learning offer over classical algorithms?
