MicroAlgo Inc. Develops Multi-Simulator Collaborative Algorithm Based on Subgraph Isomorphism to Enhance Quantum Computer Performance Using Distributed Computing Advantages

Core Concept - MicroAlgo Inc. has introduced a multi-simulator collaborative algorithm based on subgraph isomorphism to enhance quantum computing performance by overcoming qubit limitations and utilizing distributed computing [1][13]. Algorithm Overview - The algorithm decomposes large quantum circuits into smaller sub-circuits, allowing for parallel and distributed computing across multiple quantum devices [2][6]. - It employs subgraph isomorphism algorithms to identify subgraph structures within quantum circuits, ensuring that each sub-circuit operates independently and efficiently [3][5]. Computational Efficiency - The partitioning strategy optimizes computational efficiency by ensuring that sub-circuits do not interfere with each other during parallel execution [4][7]. - Quantum circuit optimization techniques are applied to reduce computational complexity and enhance execution efficiency [8]. Result Validation - MicroAlgo has conducted tests demonstrating that the results from the parallel execution of sub-circuits match those from a single quantum computer, validating the algorithm's effectiveness [10][11]. - The algorithm has been tested on various types of quantum circuits, proving its capability to handle both simple and complex circuits efficiently [12]. Future Potential - The multi-simulator collaborative algorithm is expected to play a significant role in advancing quantum computing applications and may be integrated with other quantum algorithms for more complex tasks [14][15]. - MicroAlgo aims to further optimize the algorithm for large-scale quantum circuits, enhancing its scalability and application in various fields [14][15]. Company Background - MicroAlgo Inc. focuses on developing bespoke central processing algorithms and provides solutions that integrate these algorithms with software or hardware to improve customer satisfaction and operational efficiency [16].