WiMi Hologram Cloud Inc pioneers a quantum machine learning algorithm for efficient training of large-scale models. It pre-trains dense neural networks classically, constructs sparse counterparts, and applies a quantum ordinary differential equation framework with Kalman filtering to accelerate computation and ensure stability. This integration reduces complexity and energy use, enabling rapid, scalable AI model development.

Key points

• Classical pre-training of dense neural networks extracts essential data features before sparsification.
• Quantum ODE framework with sparsity and dissipation constraints accelerates training complexity.
• Quantum Kalman filtering linearizes and stabilizes state evolution, with measurement-based parameter extraction optimizing sparse networks.

Why it matters: This hybrid quantum-classical algorithm cuts training complexity and energy use, enabling scalable, sustainable AI beyond classical limits.