MicroAlgo (MLGO, Financial) is forging ahead with the integration of quantum algorithms and machine learning to enhance quantum acceleration applications. Their approach involves a comprehensive process that includes problem modeling, quantum circuit design, experimental validation, and iterative optimization.
When tackling specific machine learning tasks, the company transforms classical data into quantum state inputs using methods such as amplitude and density matrix encoding, allowing for the mapping of feature vectors into a quantum framework. Quantum circuits are then tailored to task-specific requirements. This involves the use of variational quantum algorithms to create adjustable, parameterized quantum gate sequences. Classical optimizers are employed to fine-tune these parameters, ensuring the minimization of the target function.
Execution of quantum computations occurs on quantum computers or through cloud platforms, resulting in quantum measurements that are translated back into classical data outputs. The process further involves validating model performance with classical post-processing, examining error sources, and optimizing the quantum circuit's structure and parameters for enhanced efficiency.
