MicroAlgo Inc. Develops Classical Boosted Quantum Optimization Algorithm (CBQOA)
MicroAlgo Inc. (NASDAQ: MLGO) has announced the development of the Classical Boosted Quantum Optimization Algorithm (CBQOA), an innovative technology that combines classical and quantum computing approaches for solving complex optimization problems.
The CBQOA integrates classical optimization methods to quickly identify initial feasible solutions, followed by quantum computing refinement using Continuous-Time Quantum Walk (CTQW). This hybrid approach addresses key limitations of traditional quantum algorithms when dealing with constrained optimization problems, particularly in areas like portfolio optimization, logistics scheduling, and network routing.
Unlike standard quantum optimization algorithms such as QAOA and VQE, CBQOA maintains optimization searches within feasible solution spaces without modifying cost functions, resulting in improved efficiency and solution quality. The algorithm demonstrates practical applications in solving various problems including Maximum Cut Problem, Maximum Independent Set Problem, and Minimum Vertex Cover.
MicroAlgo Inc. (NASDAQ: MLGO) ha annunciato lo sviluppo del Classical Boosted Quantum Optimization Algorithm (CBQOA), una tecnologia innovativa che combina approcci di calcolo classico e quantistico per risolvere problemi complessi di ottimizzazione.
Il CBQOA integra metodi classici di ottimizzazione per identificare rapidamente soluzioni iniziali ammissibili, seguiti da un raffinamento tramite calcolo quantistico usando il Continuous-Time Quantum Walk (CTQW). Questo approccio ibrido supera le principali limitazioni degli algoritmi quantistici tradizionali nei problemi di ottimizzazione vincolata, specialmente in ambiti come l’ottimizzazione di portafogli, la pianificazione logistica e il routing di rete.
A differenza degli algoritmi quantistici standard come QAOA e VQE, il CBQOA mantiene la ricerca di ottimizzazione all’interno degli spazi di soluzioni ammissibili senza modificare le funzioni di costo, migliorando così efficienza e qualità delle soluzioni. L’algoritmo mostra applicazioni pratiche nella risoluzione di vari problemi, tra cui il Problema del Taglio Massimo, il Problema dell’Insieme Indipendente Massimo e la Copertura Minima dei Vertici.
MicroAlgo Inc. (NASDAQ: MLGO) ha anunciado el desarrollo del Classical Boosted Quantum Optimization Algorithm (CBQOA), una tecnología innovadora que combina enfoques de computación clásica y cuántica para resolver problemas complejos de optimización.
El CBQOA integra métodos clásicos de optimización para identificar rápidamente soluciones iniciales factibles, seguido de un refinamiento mediante computación cuántica utilizando Continuous-Time Quantum Walk (CTQW). Este enfoque híbrido aborda las principales limitaciones de los algoritmos cuánticos tradicionales al tratar problemas de optimización con restricciones, especialmente en áreas como la optimización de carteras, la planificación logística y el enrutamiento de redes.
A diferencia de algoritmos cuánticos estándar como QAOA y VQE, CBQOA mantiene las búsquedas de optimización dentro de espacios de soluciones factibles sin modificar las funciones de costo, lo que resulta en una mayor eficiencia y calidad de las soluciones. El algoritmo demuestra aplicaciones prácticas en la resolución de diversos problemas, incluyendo el Problema de Corte Máximo, el Problema del Conjunto Independiente Máximo y la Cubierta Mínima de Vértices.
MicroAlgo Inc. (NASDAQ: MLGO)는 복잡한 최적화 문제 해결을 위해 고전 컴퓨팅과 양자 컴퓨팅 방식을 결합한 혁신적인 기술인 Classical Boosted Quantum Optimization Algorithm (CBQOA) 개발을 발표했습니다.
CBQOA는 초기 실행 가능한 해를 빠르게 찾기 위해 고전 최적화 방법을 통합하고, 그 후 Continuous-Time Quantum Walk (CTQW)를 활용한 양자 컴퓨팅 정제를 수행합니다. 이 하이브리드 접근법은 포트폴리오 최적화, 물류 일정 계획, 네트워크 라우팅 등 제약 조건이 있는 최적화 문제에서 기존 양자 알고리즘의 주요 한계를 극복합니다.
QAOA 및 VQE와 같은 표준 양자 최적화 알고리즘과 달리, CBQOA는 비용 함수를 변경하지 않고 실행 가능한 해 공간 내에서 최적화 탐색을 유지하여 효율성과 해의 품질을 향상시킵니다. 이 알고리즘은 최대 절단 문제, 최대 독립 집합 문제, 최소 정점 덮개 문제 등 다양한 문제 해결에 실용적으로 적용됩니다.
MicroAlgo Inc. (NASDAQ : MLGO) a annoncé le développement du Classical Boosted Quantum Optimization Algorithm (CBQOA), une technologie innovante combinant les approches classiques et quantiques pour résoudre des problèmes complexes d’optimisation.
Le CBQOA intègre des méthodes classiques d’optimisation pour identifier rapidement des solutions initiales réalisables, suivies d’un affinage par calcul quantique utilisant le Continuous-Time Quantum Walk (CTQW). Cette approche hybride surmonte les principales limites des algorithmes quantiques traditionnels dans les problèmes d’optimisation contraints, notamment dans des domaines tels que l’optimisation de portefeuille, la planification logistique et le routage réseau.
Contrairement aux algorithmes quantiques standards comme QAOA et VQE, le CBQOA maintient les recherches d’optimisation dans des espaces de solutions réalisables sans modifier les fonctions de coût, ce qui améliore l’efficacité et la qualité des solutions. L’algorithme présente des applications pratiques pour résoudre divers problèmes, notamment le problème du maximum cut, le problème de l’ensemble indépendant maximum et la couverture minimale de sommets.
MicroAlgo Inc. (NASDAQ: MLGO) hat die Entwicklung des Classical Boosted Quantum Optimization Algorithm (CBQOA) angekündigt, einer innovativen Technologie, die klassische und Quantencomputing-Ansätze kombiniert, um komplexe Optimierungsprobleme zu lösen.
Der CBQOA integriert klassische Optimierungsmethoden, um schnell erste zulässige Lösungen zu identifizieren, gefolgt von einer Verfeinerung durch Quantencomputing mittels Continuous-Time Quantum Walk (CTQW). Dieser hybride Ansatz adressiert wesentliche Einschränkungen herkömmlicher Quantenalgorithmen bei der Behandlung von Optimierungsproblemen mit Nebenbedingungen, insbesondere in Bereichen wie Portfolio-Optimierung, Logistikplanung und Netzwerk-Routing.
Im Gegensatz zu Standard-Quantenoptimierungsalgorithmen wie QAOA und VQE hält CBQOA die Optimierungssuchen innerhalb zulässiger Lösungsräume aufrecht, ohne Kostenfunktionen zu verändern, was zu verbesserter Effizienz und Lösungsqualität führt. Der Algorithmus zeigt praktische Anwendungen bei der Lösung verschiedener Probleme, darunter das Maximum-Cut-Problem, das Maximum Independent Set-Problem und das Minimum Vertex Cover.
- Development of innovative hybrid quantum-classical optimization technology
- Potential applications across multiple high-value industries
- Technology addresses key limitations of current quantum optimization algorithms
- Reduced hardware requirements compared to traditional quantum approaches
- No immediate revenue impact disclosed
- Technology still in development phase with no commercial implementation timeline
- Competitive landscape in quantum computing optimization remains intense
Insights
MicroAlgo's quantum-classical hybrid algorithm represents significant technical innovation for solving complex optimization problems across industries.
MicroAlgo's new Classical Boosted Quantum Optimization Algorithm (CBQOA) addresses a fundamental limitation in quantum computing applications. Current quantum algorithms like QAOA and VQE struggle with constrained optimization problems, requiring costly modifications to cost functions and often producing infeasible solutions that waste computational resources.
The technical innovation of CBQOA lies in its hybrid architecture. It first employs classical optimization methods (greedy algorithms, simulated annealing) to identify initial feasible solutions, then deploys Continuous-Time Quantum Walk (CTQW) to efficiently explore that solution space. This ensures quantum states remain within feasible subspaces throughout computation.
CBQOA's approach is particularly clever because it:
- Leverages mature classical techniques to reduce quantum hardware demands
- Utilizes quantum superposition to explore multiple solutions simultaneously
- Evolves directly within feasible subspaces without explicit solution encoding
- Integrates classical evaluation mechanisms to ensure constraint satisfaction
The algorithm targets critical application domains including portfolio optimization, logistics scheduling, network routing, and protein folding – all areas where even small improvements in optimization quality can deliver substantial real-world value.
This development represents a pragmatic approach to quantum algorithm design that acknowledges current hardware limitations while establishing a foundation for more powerful applications as quantum hardware matures.
MicroAlgo's CBQOA demonstrates R&D prowess in quantum computing applications, potentially positioning the company in high-value enterprise markets.
MicroAlgo's development of the Classical Boosted Quantum Optimization Algorithm (CBQOA) showcases the company's technical capabilities in the rapidly evolving quantum computing space. The algorithm specifically targets combinatorial optimization problems – a class of computational challenges with massive commercial relevance across multiple industries.
The addressable markets for this technology are substantial:
- Financial services (portfolio optimization)
- Transportation and logistics (scheduling, routing)
- Telecommunications (network configuration)
- Biotechnology (protein folding)
By creating a hybrid approach that works within current quantum hardware constraints, MicroAlgo demonstrates strategic thinking in bridging near-term technological limitations with long-term quantum computing potential. The CBQOA framework allows for the integration of different classical optimization strategies based on problem type, making it adaptable to various enterprise use cases.
What's particularly noteworthy is how this technology positions MicroAlgo at the intersection of classical computing and quantum computing – potentially allowing the company to deliver value even before fully-realized quantum computers become commercially viable.
This announcement represents a significant step in MicroAlgo's technology roadmap, potentially differentiating the company in the enterprise optimization solutions market with a forward-looking approach that bridges classical and quantum paradigms.
Combinatorial optimization problems are widely prevalent in practical applications, such as portfolio optimization, logistics scheduling, network routing, and protein folding. In recent years, quantum computing has been regarded as a crucial tool for tackling these complex optimization challenges. Notable among these are heuristic algorithms like the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE). However, these algorithms often face significant challenges when dealing with constrained optimization problems:
For instance, classical optimization problems typically involve numerous constraints. Standard quantum optimization algorithms need to indirectly incorporate these constraints by modifying the cost function, which sharply increases the complexity of the solution process. Moreover, existing quantum algorithms struggle to ensure that the optimization search remains within the feasible solution space, resulting in wasted computational resources and the emergence of non-physical solutions. Classical optimization techniques, having matured over many years, already possess formidable problem-solving capabilities. Thus, effectively combining the strengths of classical and quantum computing has become a critical issue. MicroAlgo's CBQOA, by integrating the efficient search capabilities of classical optimization algorithms with the global search characteristics of quantum computing, paves a new path in the field of combinatorial optimization.
The core idea of MicroAlgo's CBQOA is to first leverage classical optimization methods to quickly identify high-quality feasible solutions, then utilize quantum computing techniques to further refine these solutions within their neighborhoods, aiming to find even better outcomes.
Under the CBQOA framework, efficient classical optimization algorithms—such as greedy algorithms, heuristic algorithms, simulated annealing, or local search—are initially employed to tackle the optimization problem. These classical methods, which have been extensively studied, can deliver relatively optimal feasible solutions within polynomial time, laying the groundwork for subsequent quantum computing steps. The primary task of classical optimization is to generate an initial solution and construct a feasible solution subspace. Different classical optimization strategies can be selected based on the problem type. For example:
Maximum Cut Problem (Max-Cut): A heuristic algorithm can first generate an initial partition, followed by quantum computing to identify a superior cut.
Maximum Independent Set Problem (MIS): A greedy algorithm can be used to find a sizable independent set, with quantum computing then exploring better independent set configurations.
Minimum Vertex Cover (MVC): A classical algorithm can determine a preliminary coverage scheme, which is then fine-tuned using quantum computing.
After obtaining feasible solutions from classical optimization, MicroAlgo CBQOA employs Continuous-Time Quantum Walk (CTQW) to search the solution space. CTQW is a random walk model in quantum computing, well-suited for efficiently searching feasible solutions in combinatorial optimization problems.
In CBQOA, quantum states propagate efficiently within the feasible solution space. Since CTQW employs Hamiltonian evolution, its search paths align with the problem's structure, reducing the likelihood of ineffective searches. Additionally, search efficiency is enhanced through coherent superposition; the quantum superposition property allows the system to explore multiple solutions simultaneously, increasing the probability of identifying the global optimum. Furthermore, CBQOA reduces reliance on indexing feasible solutions. Unlike QAOA, which requires explicit encoding of feasible solutions, CTQW evolves directly within the feasible subspace, avoiding dependence on solution indexing.
After the quantum optimization search, the optimal solution is obtained by measuring the quantum state. At this stage, CBQOA integrates the evaluation mechanisms of classical optimization to filter the measurement results, ensuring that the final solution satisfies the constraints and achieves optimality.
The introduction of MicroAlgo's Classical Boosted Quantum Optimization Algorithm (CBQOA) marks the dawn of a new era in the fusion of quantum and classical computing for optimization. For a long time, while quantum optimization algorithms have demonstrated immense potential, their practical application in solving constrained optimization problems has been hampered by challenges related to hardware development and algorithmic complexity. CBQOA cleverly combines classical optimization methods with quantum computing techniques, successfully circumventing the traditional quantum optimization algorithms' heavy reliance on cost functions. It ensures that the search process remains confined to the feasible solution subspace, thereby improving optimization efficiency and solution quality. This innovative approach not only leverages the mature techniques of classical optimization to lower the hardware demands on quantum computing but also utilizes Continuous-Time Quantum Walk (CTQW) to efficiently explore the solution space. This provides a more practical and feasible solution for combinatorial optimization problems. The breakthrough of this algorithm lies in its departure from purely quantum optimization; instead, it employs classical techniques to overcome the current limitations of quantum computing, marking a significant step forward in the application of quantum computing to optimization challenges.
MicroAlgo's CBQOA not only provides a practical and feasible development path for quantum optimization but also further propels quantum computing from theoretical research into real-world applications. As the hardware and software ecosystems of quantum computing continue to mature, CBQOA is expected to exert a profound impact across multiple industries, particularly in addressing complex optimization problems, potentially becoming a core component of next-generation optimization algorithms. At the same time, the development of this technology offers fresh perspectives for interdisciplinary research, fostering the integration of fields such as computer science, operations research, physics, and artificial intelligence. In the forthcoming era of quantum computing, hybrid optimization approaches like CBQOA will serve as a critical driving force for industry transformation, providing humanity with unprecedentedly powerful tools to tackle complex computational challenges.
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
FAQ
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