WiMi Hologram Cloud, a leading global Hologram Augmented Reality (“AR”) Technology provider, announced that they are actively exploring a shallow hybrid quantum-classical convolutional neural network model, bringing innovative breakthroughs to the field of image classification.
Variational quantum methods, as an important technical means in the field of quantum computing, provide effective pathways for the design and implementation of quantum algorithms by transforming the optimization problems of quantum states into classical optimization problems. WiMi has adopted an enhanced variational quantum method in the SHQCNN model, laying a solid foundation for the model’s efficient operation in image classification tasks. The enhanced variational quantum method has undergone multi-faceted optimizations based on traditional methods. First, in terms of quantum state representation, by introducing more complex combinations of quantum gates and parameterized forms, it can more precisely describe the quantum features of image data. Second, in the optimization algorithm, advanced adaptive optimization strategies are employed, which can dynamically adjust optimization parameters based on real-time feedback during the training process, accelerating convergence speed and improving the model’s training efficiency. This enhanced variational quantum method enables the SHQCNN model to fully leverage the advantages of quantum computing when handling image classification tasks, while avoiding the complexity issues brought by increasing layers in traditional QNNs.
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In image classification tasks, the quality and distinguishability of input data directly affect the model’s performance. The SHQCNN model adopts the kernel encoding method in the input layer; this method is like a precise key, enhancing the efficiency of data distinction and processing. The core idea of the kernel encoding method is to map the original image data from low-dimensional space to high-dimensional feature space through nonlinear mapping, making image data that is difficult to distinguish in the low-dimensional space easier to separate in the high-dimensional space. Through the kernel encoding method, the SHQCNN model optimizes the data processing at the input stage, providing high-quality input for the computations of subsequent hidden and output layers, thereby improving the classification accuracy of the entire model.
And the hidden layer, as the core part of the neural network, undertakes the important task of feature extraction and transformation of input data.
In traditional QNNs, as the number of layers increases, the computational complexity of the hidden layer rises sharply, leading to the training process becoming extremely difficult. The SHQCNN model designs variational quantum circuits in the hidden layer, cleverly solving this problem. Variational quantum circuits are composed of a series of quantum gates, which can perform specific transformations on the input quantum states. Compared to the hidden layers of traditional deep neural networks, variational quantum circuits have a more concise structure and lower computational complexity. Through rationally designing the types and arrangement order of quantum gates, variational quantum circuits can achieve effective extraction of image features within fewer layers.At the same time, the parameters of the variational quantum circuit can be trained through classical optimization algorithms, enabling the model to perform adaptive optimization based on different image classification tasks, further improving the model’s generalization ability.
The output layer, as the final module of the neural network, is responsible for classification decisions on the features extracted by the hidden layer. The SHQCNN model adopts the mini-batch gradient descent algorithm in the output layer; this innovative application of the algorithm brings significant improvements to the model’s parameter training and learning speed. The mini-batch gradient descent algorithm is a variant of the gradient descent algorithm. In each iteration, instead of using all the training data, it randomly selects a small batch of data from the training set for computation. Compared to the traditional batch gradient descent algorithm, the mini-batch gradient descent algorithm has faster computation speed and better convergence. In the SHQCNN model, by performing weight updates more frequently, the mini-batch gradient descent algorithm can timely adjust the model’s parameters, enabling the model to adapt more quickly to changes in the training data.
The shallow hybrid quantum-classical convolutional neural network model (SHQCNN) researched by WiMi, through the integrated application of a series of advanced technologies such as enhanced variational quantum methods, kernel encoding methods, variational quantum circuits, and mini-batch gradient descent algorithms, has significant advantages in terms of stability, accuracy, and generalization, and will bring new solutions to the field of image classification. With the continuous development of quantum computing technology and the continuous expansion of application scenarios, the SHQCNN model will demonstrate its enormous potential in more fields.
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