- MicroAlgo Inc. (MLGO, Financial) unveils Classical Boosted Quantum Optimization Algorithm (CBQOA).
- CBQOA seamlessly integrates classical and quantum computing to solve complex optimization problems.
- The innovation addresses quantum algorithm limitations, enhancing efficiency and solution quality.
MicroAlgo Inc. (NASDAQ: MLGO) has announced the development of the Classical Boosted Quantum Optimization Algorithm (CBQOA), an advanced technology that synergizes classical and quantum computing strategies to tackle intricate optimization challenges. This hybrid approach aims to improve solution efficiency and quality by leveraging existing classical optimization techniques followed by refinement through quantum computing.
The CBQOA operates by initially deploying classical methods to rapidly identify feasible solutions, which are then enhanced using Continuous-Time Quantum Walk (CTQW) within the quantum computing domain. This process uniquely addresses the constraints faced by traditional quantum algorithms, which often struggle with optimization problems that involve multiple constraints and tend to require modification of cost functions.
MicroAlgo's innovation stands out as it maintains optimization searches within feasible solution spaces, unlike standard quantum approaches such as Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), which frequently lead to computational inefficiencies. The CBQOA is particularly effective in practical applications such as portfolio optimization, logistics scheduling, and network routing, offering a pragmatic solution that potentially reshapes the landscape of combinatorial optimization problems.
By bridging the gap between classical and quantum computing, CBQOA reduces the dependency on complex quantum hardware requirements, making it a viable solution for current technological constraints. As quantum computing technology continues to evolve, innovations like CBQOA are anticipated to play a critical role across various industries, serving as a fundamental component for next-generation optimization algorithms.
