- MicroAlgo Inc. (MLGO, Financial) launches innovative classifier auto-optimization technology using Variational Quantum Algorithms (VQA).
- Key advancements include Adaptive Circuit Pruning, Hamiltonian Transformation Optimization, and Quantum Entanglement Regularization.
- The new technology reduces computational complexity by at least an order of magnitude with maintained classification accuracy.
MicroAlgo Inc. (MLGO), a company known for its central processing algorithm solutions, has announced the development of an innovative classifier auto-optimization technology based on Variational Quantum Algorithms (VQA). This new technology aims to address significant challenges in quantum machine learning by reducing the complexity of parameter updates during training, which leads to improved computational efficiency.
The classifier auto-optimization technology features three key innovations: Adaptive Circuit Pruning (ACP), Hamiltonian Transformation Optimization (HTO), and Quantum Entanglement Regularization (QER). These innovations collectively tackle issues such as high optimization complexity and the sensitivity of current quantum machine learning models to noise.
Adaptive Circuit Pruning dynamically adjusts the quantum circuit structure, eliminating redundant parameters and thus lowering computational complexity while preserving the classifier's performance. Hamiltonian Transformation Optimization alters the representation of the variational quantum circuit, which improves optimization efficiency and can reduce computational complexity by at least an order of magnitude.
Moreover, Quantum Entanglement Regularization prevents model overfitting by dynamically adjusting the entanglement strength during training, enhancing the generalization capability of the classifier on unseen data. The incorporation of Variational Quantum Error Correction (VQEC) strengthens noise resilience, making the technology more robust and reliable in real-world quantum computing environments.
MicroAlgo’s breakthrough technology not only enhances training speed and generalization capability but also sets the stage for future advancements in quantum machine learning as quantum computing hardware continues to evolve. This innovation signifies a crucial step forward in the practical implementation of quantum intelligent computing, potentially expanding its application domains across various industries.
