SHENZHEN, China, April 24, 2025 /PRNewswire/ — MicroAlgo Inc. (the “Company” or “MicroAlgo”) (NASDAQ: MLGO), announced today the development of an innovative technology, the Classical Boosted Quantum Optimization Algorithm (CBQOA). This algorithm integrates the search capabilities of classical computing with the parallel computing characteristics of quantum computing, effectively addressing constrained optimization problems without modifying the cost function. It ensures that the evolution of quantum states remains confined within the feasible subspace, providing a more efficient solution for combinatorial optimization problems.
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.
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