MicroAlgo Inc. Develops Hybrid Classical-Quantum Algorithms to Optimize Multi-Query Problems
MicroAlgo Inc. (NASDAQ: MLGO) has announced the development of an innovative hybrid classical-quantum algorithm designed to optimize Multi-Query Optimization (MQO) problems. The algorithm combines classical computing stability with quantum computing efficiency, achieving nearly 99% qubit efficiency.
The solution addresses current quantum computing limitations through key features including efficient qubit usage, reduced error rates through classical error correction mechanisms, scalability for various problem sizes, and compatibility with existing gate-based quantum computers. The algorithm transforms MQO problems into quantum-compatible forms, utilizing quantum circuits for operations while employing classical computers for error correction and result processing.
Experimental evaluations demonstrate the algorithm's capability to handle smaller-scale problems effectively, showing significant efficiency improvements compared to quantum annealing-based computers. The development represents a practical advancement in quantum computing applications, particularly for data-intensive NP-hard problems in database optimization, machine learning, and network routing.
MicroAlgo Inc. (NASDAQ: MLGO) ha annunciato lo sviluppo di un innovativo algoritmo ibrido classico-quantistico progettato per ottimizzare i problemi di Multi-Query Optimization (MQO). L'algoritmo combina la stabilità del calcolo classico con l'efficienza del calcolo quantistico, raggiungendo quasi un 99% di efficienza dei qubit.
La soluzione affronta le attuali limitazioni del calcolo quantistico attraverso caratteristiche chiave che includono l'uso efficiente dei qubit, tassi di errore ridotti grazie a meccanismi classici di correzione degli errori, scalabilità per diverse dimensioni dei problemi e compatibilità con i computer quantistici basati su gate esistenti. L'algoritmo trasforma i problemi MQO in forme compatibili con il quantistico, utilizzando circuiti quantistici per le operazioni mentre impiega computer classici per la correzione degli errori e l'elaborazione dei risultati.
Le valutazioni sperimentali dimostrano la capacità dell'algoritmo di gestire efficacemente problemi su scala ridotta, mostrando significativi miglioramenti di efficienza rispetto ai computer basati su annealing quantistico. Lo sviluppo rappresenta un avanzamento pratico nelle applicazioni del calcolo quantistico, particolarmente per problemi NP-hard ad alta intensità di dati nell’ottimizzazione di database, nell'apprendimento automatico e nel routing di rete.
MicroAlgo Inc. (NASDAQ: MLGO) ha anunciado el desarrollo de un innovador algoritmo híbrido clásico-cuántico diseñado para optimizar problemas de Multi-Query Optimization (MQO). El algoritmo combina la estabilidad de la computación clásica con la eficiencia de la computación cuántica, logrando casi un 99% de eficiencia en qubits.
La solución aborda las limitaciones actuales de la computación cuántica a través de características clave que incluyen el uso eficiente de qubits, tasas de error reducidas mediante mecanismos clásicos de corrección de errores, escalabilidad para varios tamaños de problemas y compatibilidad con ordenadores cuánticos basados en puertas existentes. El algoritmo transforma los problemas de MQO en formas compatibles con cuántico, utilizando circuitos cuánticos para las operaciones mientras emplea computadoras clásicas para la corrección de errores y el procesamiento de resultados.
Las evaluaciones experimentales demuestran la capacidad del algoritmo para manejar problemas a menor escala de manera efectiva, mostrando mejoras significativas en eficiencia en comparación con los computadores basados en recocido cuántico. El desarrollo representa un avance práctico en las aplicaciones de la computación cuántica, particularmente para problemas NP-hard intensivos en datos en la optimización de bases de datos, el aprendizaje automático y el enrutamiento de redes.
마이크로알고 Inc. (NASDAQ: MLGO)는 다중 쿼리 최적화(MQO) 문제를 최적화하기 위해 설계된 혁신적인 혼합 고전-양자 알고리즘의 개발을 발표했습니다. 이 알고리즘은 고전 컴퓨팅의 안정성과 양자 컴퓨팅의 효율성을 결합하여 거의 99%의 큐비트 효율성을 달성합니다.
이 솔루션은 효율적인 큐비트 사용, 고전 오류 수정 메커니즘을 통한 오류율 감소, 다양한 문제 크기에 대한 확장성, 기존의 게이트 기반 양자 컴퓨터와의 호환성 등 핵심 기능을 통해 현재 양자 컴퓨팅의 한계를 극복합니다. 알고리즘은 MQO 문제를 양자 호환 형태로 변환하여 작업을 위해 양자 회로를 활용하고, 오류 수정 및 결과 처리를 위해 고전 컴퓨터를 사용합니다.
실험 평가 결과, 이 알고리즘이 소규모 문제를 효과적으로 처리할 수 있는 능력을 보여주며, 양자 어닐링 기반 컴퓨터에 비해 상당한 효율성 향상을 보여주고 있습니다. 이 개발은 데이터 집약적인 NP-hard 문제, 특히 데이터베이스 최적화, 머신 러닝 및 네트워크 라우팅과 관련된 양자 컴퓨팅 응용 프로그램에서 실질적인 진전을 나타냅니다.
MicroAlgo Inc. (NASDAQ: MLGO) a annoncé le développement d'un algorithme hybride classique-quantique innovant conçu pour optimiser les problèmes de Multi-Query Optimization (MQO). L'algorithme combine la stabilité du calcul classique avec l'efficacité du calcul quantique, atteignant près de 99 % d'efficacité des qubits.
Cette solution répond aux limitations actuelles de l'informatique quantique grâce à des caractéristiques clés parmi lesquelles l'utilisation efficace des qubits, des taux d'erreur réduits grâce à des mécanismes classiques de correction des erreurs, la scalabilité pour diverses tailles de problèmes, et la compatibilité avec les ordinateurs quantiques existants basés sur des portes. L'algorithme transforme les problèmes de MQO en formes compatibles avec le quantique, utilisant des circuits quantiques pour les opérations tout en employant des ordinateurs classiques pour la correction des erreurs et le traitement des résultats.
Les évaluations expérimentales montrent la capacité de l'algorithme à gérer efficacement des problèmes à petite échelle, avec des améliorations significatives en termes d'efficacité par rapport à des ordinateurs basés sur l'annealing quantique. Ce développement représente une avancée pratique dans les applications de l'informatique quantique, en particulier pour les problèmes NP-difficiles à forte intensité de données dans l'optimisation des bases de données, l'apprentissage automatique et le routage des réseaux.
MicroAlgo Inc. (NASDAQ: MLGO) hat die Entwicklung eines innovativen hybriden klassisch-quantischen Algorithmus angekündigt, der zur Optimierung von Multi-Query Optimization (MQO)-Problemen dient. Der Algorithmus kombiniert die Stabilität der klassischen Datenverarbeitung mit der Effizienz der quantischen Datenverarbeitung und erreicht nahezu 99% Qubit-Effizienz.
Die Lösung adressiert die derzeitigen Einschränkungen der Quantencomputing durch wichtige Merkmale wie effiziente Qubit-Nutzung, reduzierte Fehlerquoten durch klassische Fehlerkorrekturmechanismen, Skalierbarkeit für verschiedene Problemgrößen und Kompatibilität mit bestehenden gate-basierten Quantencomputern. Der Algorithmus transformiert MQO-Probleme in quantum-kompatible Formen, indem er quantenbasierte Schaltungen für Operationen nutzt und klassische Computer für die Fehlerkorrektur und Ergebnisauswertung einsetzt.
Experimentelle Bewertungen zeigen, dass der Algorithmus in der Lage ist, kleinere Probleme effektiv zu bewältigen und erhebliche Effizienzverbesserungen im Vergleich zu quantenannealing-basierten Computern aufzuweisen. Die Entwicklung stellt einen praktischen Fortschritt in der Anwendung von Quantencomputing dar, insbesondere bei datenintensiven NP-harten Problemen in der Datenbankoptimierung, im maschinellen Lernen und im Netzwerk-Routing.
- Achieved 99% qubit efficiency in quantum computing operations
- Successfully developed hybrid algorithm compatible with existing quantum hardware
- Demonstrated significant efficiency improvements over quantum annealing-based systems
- Successfully reduced quantum computation error rates through classical error correction
- Current implementation to smaller-scale problems due to qubit constraints
- Technology still faces practical limitations in handling large-scale problems
Insights
The development of a hybrid classical-quantum algorithm by MicroAlgo represents a pragmatic approach to current quantum computing limitations. The claimed
The algorithm's focus on Multi-Query Optimization (MQO) is strategically sound, as this is a complex NP-hard problem where quantum advantages could be significant. However, the lack of specific performance metrics compared to classical algorithms and missing details about the actual quantum circuit architecture raise questions about real-world applicability. While the compatibility with gate-based quantum computers is promising, success will heavily depend on error correction capabilities and quantum circuit depth.
From a market perspective, MicroAlgo's position in quantum-classical hybrid solutions puts them in a rapidly growing market segment, but faces significant competition from tech giants like IBM, Google and Microsoft. The company's small market cap of
The announcement lacks important details about commercialization timeline, potential revenue streams, or specific industry partnerships. While quantum computing holds immense potential, the path to monetization remains unclear. Investors should note that quantum computing stocks often experience high volatility based on technical announcements, but sustainable value creation requires clear business applications and revenue models.
Quantum computing is a technology that uses the principles of quantum mechanics to process information. Compared to traditional classical computers, quantum computers exhibit the potential to outperform classical computers in handling certain types of problems, such as search, optimization, and simulating quantum systems. However, the realization of quantum computers faces technical challenges, particularly in constructing quantum computers with a sufficient number of qubits and low error rates.
The Multi-Query Optimization (MQO) problem is a class of data-intensive problems that are NP-hard, and it has applications in many fields such as database query optimization, machine learning algorithms, and network routing. The core of the MQO problem lies in how to effectively handle multiple query requests to minimize the overall computational cost or time.
Although quantum computers theoretically have tremendous potential, current quantum computers are far from being fully practical. The limited number of qubits and high error rates restrict their ability to solve large-scale problems. To address these issues, MicroAlgo has proposed a hybrid algorithm that combines the stability of classical computers with the efficiency of quantum computers.
MicroAlgo's hybrid algorithm design is based on the following key points:
Efficient Use of Qubits: Through carefully designed quantum circuits, the algorithm ensures efficient utilization of qubits, achieving a qubit efficiency close to
Reduction of Error Rates: By integrating error correction mechanisms from classical algorithms, the error rate during the quantum computation process is significantly reduced.
Scalability of the Algorithm: The algorithm design by MicroAlgo takes scalability into account, enabling it to adapt to problems of varying sizes.
Compatibility with Existing Technologies: MicroAlgo's algorithm is compatible with existing gate-based quantum computers, meaning it can run on current hardware.
MicroAlgo's hybrid algorithm first transforms the MQO problem into a form that can be handled by quantum computing. Quantum circuits are designed to perform the necessary quantum operations, including quantum state preparation, application of quantum gates, and quantum measurement. Then, during the quantum computation process, classical computers are used to assist the quantum computation, such as in qubit error correction and post-processing of the results. Through experiments and simulations, the algorithm's performance is continuously optimized to ensure optimal performance with limited qubit resources.
MicroAlgo has conducted detailed experimental evaluations of the algorithm, including testing its performance on problems of various scales. The experimental results show that, despite the current limitations in qubit numbers, our algorithm is still able to handle smaller-scale problems and demonstrate a qubit efficiency close to
In exploring the vast field of quantum computing, MicroAlgo's hybrid algorithm represents an innovative solution that combines the stability of classical computing with the efficiency of quantum computing to address the challenges of Multi-Query Optimization (MQO). Through carefully designed quantum circuits and algorithmic optimizations, the algorithm not only improves qubit utilization efficiency but also significantly reduces error rates, enabling it to run on existing quantum hardware while maintaining scalability for large-scale problems. This achievement marks a significant step forward in the practical realization of quantum computing.
With the ongoing advancements in quantum technology, there is every reason to believe that MicroAlgo's hybrid algorithm will play an even more important role in the future. As quantum computer hardware improves and the number of qubits increases, the algorithm will be able to tackle larger-scale problems, unlocking greater potential in fields such as chemistry, physics, and machine learning.
MicroAlgo's hybrid algorithm is not only a major breakthrough in existing technology but also a powerful outlook on the future applications of quantum computing. We firmly believe that, through continuous research and innovation, quantum computing will gradually transition from theory to practice, becoming a powerful driver of technological progress and societal development. We look forward to a future where quantum computing will bring even more surprises and possibilities, opening a new era of computing.
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.
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SOURCE Microalgo.INC
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