박경덕 연구실

Quantum-inspired classification via efficient simulation of Helstrom measurement. [42] Optimizing quantum convolutional neural network architectures for arbitrary data dimension. [38] Quantum support vector data description for anomaly detection. [37] Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning. [36] The effect of classical optimizers and Ansatz depth on QAOA performance in noisy devices. [35 ] Quadratic speed-ups in quantum kernelized binary classification. [34] Quantum variational distance-based centroid classifier. [33] Scalable quantum measurement error mitigation via conditional independence and transfer learning. [32] Variational quantum state discriminator for supervised machine learning. Dongkeun Lee, Kyunghyun Baek, Joonsuk Huh, and Daniel K. Park*. [31] Classical-to-quantum convolutional neural network transfer learning. [30] Hierarchical quantum circuit representations for neural architecture search. [29] Variational quantum approximate support vector machine with inference transfer. [28] Configurable sublinear circuits for quantum state preparation. [26] Quantum-inspired algorithm for direct multi-class classification. Roberto Giuntini, Federico Holik, Daniel K. Park*, Hector Freytes, Carsten Blank, and Giuseppe Sergioli. [25] Linear-depth quantum circuits for multi-qubit controlled gates. Jihye Kim, Byungdu Oh, Yonuk Chong, Euyhyeon Hwang, Daniel K. Park*. Carsten Blank, Adenilton J. da Silva, Lucas P. de Albuquerque, Francesco Petruccione, and Daniel K. Park*. [22] Quantum convolutional neural network for classical data classification. [21] Quantum-enhanced analysis of discrete stochastic processes. [20] A divide-and-conquer algorithm for quantum state preparation. [19] Circuit-based quantum random access memory for classica