1：Experimental Design and Method Selection:

The framework involves data compression using a convolutional autoencoder, quantum pre-training of a Restricted Boltzmann Machine (RBM) on a D-Wave quantum annealer, and classical training of a neural network for image classification. The autoencoder compresses images to a size suitable for quantum processing, the RBM is trained using quantum sampling to replace classical Gibbs sampling, and the neural network is initialized with RBM weights for classification.

2：Sample Selection and Data Sources:

Four datasets are used: MNIST (grayscale 28x28 images of handwritten digits), Fashion-MNIST (grayscale 28x28 images of clothing), a medical imaging dataset (650 grayscale 512x512 images including fluoroscopic and venogram images), and a laparoscopic tool dataset (1000 RGB 596x596 images of medical tools).

3：List of Experimental Equipment and Materials:

D-Wave 2000Q quantum annealer (up to 2048 qubits), classical computers for autoencoder and neural network training, and standard machine learning libraries (e.g., for RMSProp optimizer and Adadelta optimizer).

4：Experimental Procedures and Operational Workflow:

First, train an autoencoder to compress images. Second, use the compressed data to train an RBM on the D-Wave device with 5000 sampling repetitions. Third, initialize a neural network with the RBM weights and train it classically for image classification. Evaluate using accuracy metrics over epochs.

5：Data Analysis Methods:

Classification accuracy is computed as the number of successful classifications over total classifications. Training curves and loss functions (mean absolute error for autoencoder, binary crossentropy for classifier) are analyzed. Comparisons are made with different initialization methods (classical RBM, quantum RBM, constant, Glorot).