MicroAlgo researches quantum machine learning algorithms

MicroAlgo (MLGO) announced that quantum algorithms will be deeply integrated with machine learning to explore practical application scenarios for quantum acceleration. MicroAlgo’s development of quantum machine learning technology follows a closed-loop process of “problem modeling – quantum circuit design – experimental validation – optimization iteration.” For specific machine learning tasks, the team preprocesses classical data into quantum state inputs, mapping feature vectors into a quantum system using techniques like amplitude encoding or density matrix encoding. Quantum circuits are designed based on task requirements, for instance, by employing variational quantum algorithms to construct trainable parameterized quantum gate sequences, with a classical optimizer adjusting the quantum circuit parameters to minimize the target function. During the quantum computing execution phase, the circuits are run on a quantum computer or cloud platform, and quantum measurement results are obtained and converted into classical data outputs.Validate model performance through classical post-processing, analyze error sources, and reverse optimize quantum circuit structure and parameters.