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MicroAlgo Inc. Develops Multi-Simulator Collaborative Algorithm Based on Subgraph Isomorphism to Enhance Quantum Computer Performance Using Distributed Computing Advantages
SHENZHEN, China, March 17, 2025 /PRNewswire/ -- MicroAlgo Inc. (NASDAQ: MLGO), (the "Company"or "MicroAlgo"), today announced the introduction of an innovative solution: a multi-simulator collaborative algorithm based on subgraph isomorphism, aimed at overcoming the limitations of qubit numbers and leveraging the advantages of distributed computing to enhance the performance of quantum computers.
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.
