MicroCloud Hologram develops quantum state preparation algorithm
MicroCloud Hologram Inc. (NASDAQ: HOLO) announced the development of a proprietary technology for approximate quantum state preparation and an entanglement-dependent complexity algorithm, according to a company press release.
The technology restructures the quantum state preparation workflow to shift computational complexity from quantum circuits to classical computing systems. Combined with entanglement structure analysis, the framework generates controllable-depth approximate quantum states intended for use on noisy intermediate-scale quantum devices.
The technical framework operates across three layers. A classical computing layer performs structural analysis and amplitude rearrangement using tensor decomposition. A second layer constructs hierarchical parameterized quantum circuits organized into modular entanglement subspaces, with circuit depth scaling linearly with the number of core entanglement blocks rather than exponentially with data dimensionality. A third layer uses a hybrid quantum-classical iterative process in which most parameter optimization is handled classically, with quantum hardware used only for targeted corrections.
In tests on random vectors and image datasets, the company reported that the approximate preparation algorithm reduced circuit depth by more than 50% compared to exact amplitude initialization methods, while maintaining tolerable amplitude error levels on superconducting quantum processors.
In quantum machine learning classification experiments conducted under noisy conditions, models using approximate state loading achieved marginally higher classification accuracy on test datasets than those using exact state initialization, which the company attributed to noise robustness and overfitting suppression.
The company stated it plans to refine the entanglement-dependent complexity model and integrate the algorithm with quantum neural network architectures to build an end-to-end quantum data processing framework.
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