Quantum Computing

Our research group is dedicated to advancing quantum computing through the development of innovative algorithms, circuit designs, and quantum machine learning models. We focus on enhancing quantum algorithms such as the Variational Quantum Eigensolver (VQE) and Quantum Convolutional Neural Networks (QCNNs), applying them to practical problems like quantum phase recognition and pulsar classification. Additionally, we are pioneering work in quantum neuromorphic computing with the introduction of a quantum leaky integrate-and-fire (QLIF) neuron model. Our efforts also include developing robust quantum circuit design tools and visualization techniques, as well as investigating evolutionary algorithms to optimize quantum circuits, highlighting the efficiency and performance improvements of hierarchical representations.

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