Zobrazeno 1 - 10
of 831
pro vyhledávání: '"Park, Daniel P."'
This work introduces the Schmidt quantum compressor, an innovative approach to quantum data compression that leverages the principles of Schmidt decomposition to encode quantum information efficiently. In contrast to traditional variational quantum a
Externí odkaz:
http://arxiv.org/abs/2412.16337
Autor:
Hur, Tak, Park, Daniel K.
Understanding and improving generalization capabilities is crucial for both classical and quantum machine learning (QML). Recent studies have revealed shortcomings in current generalization theories, particularly those relying on uniform bounds, acro
Externí odkaz:
http://arxiv.org/abs/2411.06919
Autor:
Kim, Yujin, Park, Daniel K.
Deterministic quantum computation with one qubit (DQC1) is of significant theoretical and practical interest due to its computational advantages in certain problems, despite its subuniversality with limited quantum resources. In this work, we introdu
Externí odkaz:
http://arxiv.org/abs/2411.02751
Quantum machine learning is emerging as a promising application of quantum computing due to its distinct way of encoding and processing data. It is believed that large-scale quantum machine learning demonstrates substantial advantages over classical
Externí odkaz:
http://arxiv.org/abs/2408.16327
Autor:
Lee, Changwon, Araujo, Israel F., Kim, Dongha, Lee, Junghan, Park, Siheon, Ryu, Ju-Young, Park, Daniel K.
Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis. This approach is particularly attractive due to the absence of the barren pl
Externí odkaz:
http://arxiv.org/abs/2403.19099
Autor:
Lee, Jungyun, Park, Daniel K.
Publikováno v:
Adv Quantum Technol. 2024, 2400126
Classification is at the core of data-driven prediction and decision-making, representing a fundamental task in supervised machine learning. Recently, several quantum machine learning algorithms that use quantum kernels as a measure of similarities b
Externí odkaz:
http://arxiv.org/abs/2403.17453
The Helstrom measurement (HM) is known to be the optimal strategy for distinguishing non-orthogonal quantum states with minimum error. Previously, a binary classifier based on classical simulation of the HM has been proposed. It was observed that usi
Externí odkaz:
http://arxiv.org/abs/2403.15308
Autor:
Choi, Junggu, Hur, Tak, Park, Daniel K., Shin, Na-Young, Lee, Seung-Koo, Lee, Hakbae, Han, Sanghoon
Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study explores the application of a hybrid quantum-classical algorithm for cl
Externí odkaz:
http://arxiv.org/abs/2405.01554
Publikováno v:
Phys. Rev. A 110, 022411 (2024)
Quantum embedding is a fundamental prerequisite for applying quantum machine learning techniques to classical data, and has substantial impacts on performance outcomes. In this study, we present Neural Quantum Embedding (NQE), a method that efficient
Externí odkaz:
http://arxiv.org/abs/2311.11412
Autor:
Oh, Hyeondo, Park, Daniel K.
Publikováno v:
Machine Learning: Science and Technology 5 035052 (2024)
Anomaly detection is a critical problem in data analysis and pattern recognition, finding applications in various domains. We introduce quantum support vector data description (QSVDD), an unsupervised learning algorithm designed for anomaly detection
Externí odkaz:
http://arxiv.org/abs/2310.06375