WiMi Unveils VQA-Based Quantum Pooling with Polynomial-Level Speedup
WIMI•WiMi is developing a variational quantum algorithm framework that integrates a Quantum Haar Transform with quantum partial measurement to enable efficient multi-dimensional data pooling. This approach promises polynomial-level computational acceleration, richer feature representation and scalability across audio, 2D images, 3D point clouds and hyperspectral data.
1. Development of VQA-Based Pooling Technology
WiMi is exploring a variational quantum algorithm (VQA) framework that combines parameterized quantum circuits and classical optimization to apply Quantum Haar Transform and quantum partial measurement for multi-dimensional data pooling.
2. Technical Advantages
The integration of QHT with quantum partial measurement preserves local features and structural correlations while reducing computational complexity to polynomial time, outperforming classical pooling by leveraging quantum superposition, entanglement and probabilistic extraction of key features.
3. Business Prospects and Applications
This technology is scalable across unstructured data types including audio, images, point clouds and hyperspectral datasets, positioning WiMi to enhance its AR services and quantum machine learning offerings as quantum hardware matures.
