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, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that they are committed to developing a hybrid quantum-classical model that combines the acceleration capabilities of quantum computing with the stability of classical computers, using differential privacy optimization algorithms to protect data privacy. The key aspect of this model lies in how to incorporate differential privacy mechanisms into the design of quantum circuits, as well as how to evaluate the balance between privacy protection effectiveness and model performance during the simulation and testing phases.
The hybrid model uses a quantum layer to handle feature extraction and complex transformations, while the classical layer is responsible for implementing differential privacy protection and making final decisions. The quantum layer leverages the superposition and entanglement properties of quantum bits to achieve efficient data encoding and information extraction. The classical layer, on the other hand, uses established differential privacy techniques by adding appropriate noise to obscure the impact of individual data points. Based on the output from the quantum layer, the classical layer applies differential privacy mechanisms such as Laplace noise or Gaussian noise to ensure that the model's sensitivity to individual data changes remains below a predefined threshold. At the same time, by adjusting the noise level and model complexity, the model seeks the optimal balance between privacy protection and model accuracy. Due to the limitations of current quantum hardware, the model is initially tested through simulation on classical computers to verify the effectiveness of the differential privacy mechanism and assess its impact on model performance.
The hybrid quantum-classical model developed by WiMi strengthens privacy protection and effectively prevents the leakage of sensitive information. The introduction of differential privacy protection technology increases the model's tolerance to noise and data perturbations, helping to improve its generalization ability and opening new pathways for the practical application of quantum computing.
In the future, with advancements in quantum technology and the deepening of differential privacy quantum machine learning theory, we anticipate the emergence of more innovative applications in fields such as medical data analysis and financial risk assessment. Quantum machine learning will usher in a new era of data science, protecting privacy while unlocking new possibilities.