Published 9 days ago • loading... • Updated 9 days ago

MicroAlgo Inc. Develops Classifier Auto-Optimization Technology Based on Variational Quantum Algorithms, Accelerating the Advancement of Quantum Machine Learning

On May 2, 2025, in Shenzhen, China, MicroAlgo Inc. Unveiled a new automated optimization solution for quantum classifiers that leverages Variational Quantum Algorithms to enhance training efficiency and reduce parameter update complexity.
The technology addresses challenges in traditional quantum classifiers, such as high computational complexity and instability caused by deep quantum circuits and noisy devices.
MicroAlgo reduces parameter update complexity by deep core circuit optimization using Adaptive Circuit Pruning, Hamiltonian Transformation Optimization, and novel regularization methods like Quantum Entanglement Regularization.
This approach lowers computational costs by at least an order of magnitude, maintains classification accuracy, improves training speed, stability, and generalization, and enhances noise resilience on real quantum devices.
The technology lays a foundation for advancing quantum machine learning, accelerating practical quantum intelligent computing, and expanding quantum computing applications amid ongoing hardware advances.

Insights by Ground AI

Does this summary seem wrong?

17 Articles

All

Left

Center

Right

LoudounTimes.com

+16 Reposted by 16 other sources

Center

MicroAlgo Inc. Develops Classifier Auto-Optimization Technology Based on Variational Quantum Algorithms, Accelerating the Advancement of Quantum Machine Learning

SHENZHEN, China, May 2, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO) announced today the launch of their latest classifier auto-optimization technology based on Variational Quantum Algorithms (VQA). This technology significantly reduces the complexity of parameter updates…

9 days ago

Read Full Article

Think freely.Subscribe and get full access to Ground NewsSubscriptions start at $9.99/year