MicroAlgo Inc. Develops Multi-Simulator Collaborative Algorithm Based on Subgraph Isomorphism to Enhance Quantum Computer Performance Using Distributed Computing Advantages
MicroAlgo Inc. (NASDAQ: MLGO) has unveiled a groundbreaking multi-simulator collaborative algorithm based on subgraph isomorphism to enhance quantum computer performance. The algorithm addresses qubit limitations by decomposing large quantum circuits into smaller sub-circuits that can be processed using distributed computing.
The technology works by analyzing quantum circuits to identify subgraph structures, partitioning them into independent sub-circuits that don't exceed available quantum computer capacity. These sub-circuits are then distributed across multiple quantum computers or simulators for parallel execution, significantly improving computational efficiency.
The algorithm employs amplitude amplification to merge results from sub-circuits into a unified output. Multiple tests have validated the algorithm's effectiveness across various quantum circuits, demonstrating successful parallel execution while maintaining result accuracy equivalent to single quantum computer processing.
MicroAlgo Inc. (NASDAQ: MLGO) ha svelato un innovativo algoritmo collaborativo multi-simulator basato sull'isomorfismo di sottografi per migliorare le prestazioni dei computer quantistici. L'algoritmo affronta le limitazioni dei qubit decomponendo grandi circuiti quantistici in sottocircuiti più piccoli che possono essere elaborati utilizzando il calcolo distribuito.
La tecnologia funziona analizzando i circuiti quantistici per identificare strutture di sottografi, suddividendoli in sottocircuiti indipendenti che non superano la capacità disponibile del computer quantistico. Questi sottocircuiti vengono quindi distribuiti su più computer quantistici o simulatori per l'esecuzione parallela, migliorando significativamente l'efficienza computazionale.
L'algoritmo utilizza l'amplificazione dell'ampiezza per unire i risultati dei sottocircuiti in un output unificato. Molti test hanno convalidato l'efficacia dell'algoritmo su vari circuiti quantistici, dimostrando un'esecuzione parallela di successo mantenendo un'accuratezza dei risultati equivalente a quella del processamento su un singolo computer quantistico.
MicroAlgo Inc. (NASDAQ: MLGO) ha presentado un innovador algoritmo colaborativo multi-simulador basado en isomorfismo de subgrafos para mejorar el rendimiento de las computadoras cuánticas. El algoritmo aborda las limitaciones de los qubits descomponiendo grandes circuitos cuánticos en subcircuitos más pequeños que pueden ser procesados utilizando computación distribuida.
La tecnología funciona analizando circuitos cuánticos para identificar estructuras de subgrafos, dividiéndolos en subcircuitos independientes que no superan la capacidad disponible de la computadora cuántica. Estos subcircuitos se distribuyen luego entre múltiples computadoras cuánticas o simuladores para su ejecución paralela, mejorando significativamente la eficiencia computacional.
El algoritmo emplea amplificación de amplitud para fusionar los resultados de los subcircuitos en una salida unificada. Múltiples pruebas han validado la efectividad del algoritmo en varios circuitos cuánticos, demostrando una ejecución paralela exitosa mientras se mantiene la precisión de los resultados equivalente al procesamiento en una sola computadora cuántica.
MicroAlgo Inc. (NASDAQ: MLGO)는 양자 컴퓨터 성능을 향상시키기 위해 서브그래프 동형성을 기반으로 한 혁신적인 다중 시뮬레이터 협업 알고리즘을 공개했습니다. 이 알고리즘은 큰 양자 회로를 분해하여 분산 컴퓨팅을 사용하여 처리할 수 있는 더 작은 서브 회로로 나누어 qubit의 한계를 해결합니다.
이 기술은 양자 회로를 분석하여 서브그래프 구조를 식별하고, 사용 가능한 양자 컴퓨터 용량을 초과하지 않는 독립적인 서브 회로로 분할하는 방식으로 작동합니다. 그런 다음 이러한 서브 회로는 여러 양자 컴퓨터 또는 시뮬레이터에 분산되어 병렬 실행되며, 계산 효율성을 크게 향상시킵니다.
이 알고리즘은 진폭 증폭을 사용하여 서브 회로의 결과를 통합된 출력으로 병합합니다. 여러 테스트를 통해 다양한 양자 회로에서 알고리즘의 효과가 검증되었으며, 단일 양자 컴퓨터 처리와 동등한 결과 정확성을 유지하면서 성공적인 병렬 실행을 보여주었습니다.
MicroAlgo Inc. (NASDAQ: MLGO) a dévoilé un algorithme collaboratif multi-simulateur révolutionnaire basé sur l'isomorphisme de sous-graphes pour améliorer les performances des ordinateurs quantiques. L'algorithme traite les limitations des qubits en décomposant de grands circuits quantiques en sous-circuits plus petits pouvant être traités à l'aide de l'informatique distribuée.
La technologie fonctionne en analysant les circuits quantiques pour identifier des structures de sous-graphes, les partitionnant en sous-circuits indépendants qui ne dépassent pas la capacité disponible de l'ordinateur quantique. Ces sous-circuits sont ensuite répartis sur plusieurs ordinateurs quantiques ou simulateurs pour une exécution parallèle, améliorant ainsi considérablement l'efficacité computationnelle.
L'algorithme utilise l'amplification d'amplitude pour fusionner les résultats des sous-circuits en une sortie unifiée. Plusieurs tests ont validé l'efficacité de l'algorithme sur divers circuits quantiques, démontrant une exécution parallèle réussie tout en maintenant une précision des résultats équivalente à celle du traitement par un seul ordinateur quantique.
MicroAlgo Inc. (NASDAQ: MLGO) hat einen bahnbrechenden multi-Simulator-Kollaborationsalgorithmus vorgestellt, der auf Subgraph-Isomorphismus basiert, um die Leistung von Quantencomputern zu verbessern. Der Algorithmus geht die Einschränkungen von Qubits an, indem er große Quanten-Schaltungen in kleinere Unter-Schaltungen zerlegt, die mit verteilter Verarbeitung bearbeitet werden können.
Die Technologie funktioniert, indem sie Quanten-Schaltungen analysiert, um Subgraph-Strukturen zu identifizieren und sie in unabhängige Unter-Schaltungen zu partitionieren, die die verfügbare Kapazität des Quantencomputers nicht überschreiten. Diese Unter-Schaltungen werden dann auf mehrere Quantencomputer oder Simulatoren verteilt, um eine parallele Ausführung zu ermöglichen, was die Recheneffizienz erheblich verbessert.
Der Algorithmus verwendet Amplitude Amplifikation, um die Ergebnisse der Unter-Schaltungen zu einem einheitlichen Output zu vereinen. Mehrere Tests haben die Wirksamkeit des Algorithmus über verschiedene Quanten-Schaltungen hinweg validiert und dabei eine erfolgreiche parallele Ausführung demonstriert, während die Ergebnisgenauigkeit, die mit der Verarbeitung durch einen einzelnen Quantencomputer vergleichbar ist, beibehalten wurde.
- Innovative solution addressing key quantum computing limitation
- Successful validation through multiple tests
- Demonstrated capability to handle both simple and complex quantum circuits
- Technology enables scalability for quantum computing applications
- None.
Insights
MicroAlgo's new multi-simulator collaborative algorithm addresses a fundamental limitation in quantum computing - the restricted number of qubits in current systems. By leveraging subgraph isomorphism from graph theory, they've created a method to effectively break large quantum circuits into smaller pieces that can run in parallel across multiple quantum computers or simulators.
This innovation solves the quantum computing equivalent of parallel processing in traditional computing. The core technological advancement here is threefold: (1) intelligent circuit partitioning using graph theory, (2) distributed computing framework for quantum tasks, and (3) amplitude amplification techniques to recombine results accurately.
What makes this particularly valuable is that MicroAlgo has validated the approach through testing, demonstrating that results match those of single-machine execution across various circuit types. This suggests the approach maintains quantum coherence despite the partitioning.
The practical implication is that quantum computing applications previously constrained by qubit limitations could now become viable with existing hardware. Rather than waiting for quantum hardware capabilities to improve, MicroAlgo's software solution potentially enables immediate scaling of quantum computing applications.
For a smaller company like MicroAlgo (
The core concept of MicroAlgo's multi-simulator collaborative subgraph isomorphism algorithm is to decompose large quantum circuits into multiple smaller sub-circuits, leveraging parallel and distributed computing techniques to distribute computation tasks across multiple quantum computers or quantum simulators. This approach effectively utilizes the limited quantum bit resources and improves the execution efficiency of quantum circuits.
The algorithm first analyzes the quantum circuit to identify subgraph structures within it. Using subgraph isomorphism algorithms from graph theory, the circuit is partitioned into several smaller sub-circuits, each containing no more qubits than the current quantum computer's capacity allows. Through optimization and matching techniques, each sub-circuit is ensured to be able to operate independently and perform computations on different quantum devices.
In the first step of quantum circuit partitioning, MicroAlgo's algorithm begins by analyzing the structure of the circuit and identifying potential subgraphs within it. This process is based on the subgraph isomorphism algorithm from graph theory. By analyzing the circuit's topological structure, the circuit is divided into several non-overlapping smaller sub-circuits. The number of qubits in each sub-circuit does not exceed the resource limits of the available quantum computers, and each sub-circuit can operate independently during computation. This partitioning strategy ensures that the parallel execution of different sub-circuits does not interfere with each other, thereby optimizing the overall computational efficiency.
The subgraph isomorphism algorithm plays a key role in this process. With this algorithm, MicroAlgo efficiently identifies subgraph structures within the circuit and uses graph matching techniques to partition the circuit into multiple sub-circuits. Each sub-circuit is assigned an independent computational task, and these tasks can be executed in parallel, significantly reducing the computation time.
Once the circuit partitioning is complete, MicroAlgo's algorithm assigns each sub-circuit to different quantum simulators or quantum computers for execution. To improve computational efficiency, the algorithm employs a distributed computing framework, efficiently distributing computational tasks across multiple computing units. Through a parallel programming model, multiple quantum computing devices can collaborate, greatly enhancing the overall computation speed.
In this process, MicroAlgo's multi-simulator collaborative algorithm takes advantage of distributed computing. The distributed computing framework not only fully utilizes the computational resources of each quantum computer but also flexibly adjusts the number of qubits in each sub-circuit as needed to achieve optimal computational performance. This strategy enables the efficient allocation of computational tasks across multiple quantum computing devices, solving the problem that a single quantum computer cannot handle large-scale circuits.
To further improve computational efficiency, MicroAlgo also applies quantum circuit optimization techniques during the partitioning of sub-circuits. The optimization process ensures that the execution efficiency of each sub-circuit is maximized, while maintaining the consistency of the final results. In this process, MicroAlgo reduces the computational complexity of each sub-circuit by optimizing the structure of the quantum circuit, further shortening the computation time.
After the computation is completed, MicroAlgo uses a technique called "amplitude amplification" to ensure that the results obtained from each sub-circuit are correctly merged. Amplitude amplification enhances the probability amplitude of specific quantum states, ensuring that when the results are combined, they accurately reflect the original circuit's computation. Through this technique, MicroAlgo successfully merges the results from multiple sub-circuits into a unified output, consistent with the result of a single quantum computer's execution, thereby demonstrating the effectiveness and correctness of the algorithm.
MicroAlgo has conducted multiple tests on its multi-simulator collaborative subgraph isomorphism algorithm to verify its effectiveness and feasibility. In these tests, MicroAlgo partitioned several quantum circuits into multiple sub-circuits and distributed them across different quantum computing devices for parallel execution. The test results showed that after partitioning and parallel execution, the results obtained from the sub-circuits matched the results of executing the circuit on a single quantum computer, proving that the algorithm successfully addresses the limitations of qubit numbers and enables efficient execution of quantum circuits across multiple quantum computing devices.
Additionally, MicroAlgo tested the algorithm on various types of quantum circuits to validate its performance across different application scenarios. The results demonstrated that MicroAlgo's algorithm is capable of handling not only simple quantum circuits but also complex ones, and it can efficiently execute them in parallel across multiple quantum devices. This provides strong support for the broader application of quantum computing in various fields.
The multi-simulator collaborative subgraph isomorphism algorithm developed by MicroAlgo provides an innovative solution for the field of quantum computing. By decomposing large quantum circuits into multiple smaller sub-circuits and utilizing distributed computing and parallel execution, it overcomes the limitations of qubit numbers and enhances the execution efficiency of quantum circuits. The successful implementation of this technology not only provides strong support for quantum computing but also paves the way for its development in practical applications.
As quantum computing technology continues to advance, MicroAlgo's multi-simulator collaborative subgraph isomorphism algorithm is expected to play a key role in more application areas. Through further optimization of the algorithm, MicroAlgo plans to enhance its potential for large-scale quantum circuits and explore integration with other quantum algorithms to address more complex computational tasks.
In the future, MicroAlgo's algorithm could potentially be combined with other quantum algorithms in fields such as quantum optimization and quantum machine learning, providing additional solutions for quantum computing. By combining the powerful computational capabilities of quantum computing with parallel and distributed computing technologies from modern computer science, MicroAlgo's algorithm can not only solve the problem of qubit limitations but also improve the scalability of quantum circuits, thereby advancing the widespread application of quantum computing technology.
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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FAQ
How does MicroAlgo's (MLGO) new quantum computing algorithm work?
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