Selective re-encoding of top PCA features inside a QCNN, plus joint optimization of a PCA-based and an autoencoder-based QCNN, improves binary image classification accuracy on MNIST and Fashion-MNIST over the paper's baselines.
Decomposition of orthogonal matrix and synthesis of two-qubit and three-qubit orthogonal gates
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abstract
The decomposition of matrices associated to two-qubit and three-qubit orthogonal gates is studied, and based on the decomposition the synthesis of these gates is investigated. The optimal synthesis of general two-qubit orthogonal gate is obtained. For two-qubit unimodular orthogonal gate, it requires at most 2 CNOT gates and 6 one-qubit Ry gates. For the general three-qubit unimodular orthogonal gate, it can be synthesized by 16 CNOT gates and 36 one-qubit Ry and Rz gates in the worst case.
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Selective Feature Re-Encoded Quantum Convolutional Neural Network with Joint Optimization for Image Classification
Selective re-encoding of top PCA features inside a QCNN, plus joint optimization of a PCA-based and an autoencoder-based QCNN, improves binary image classification accuracy on MNIST and Fashion-MNIST over the paper's baselines.