Zobrazeno 1 - 10
of 166
pro vyhledávání: '"Tzu-Chieh Wei"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Abstract Many quantum algorithms are developed to evaluate eigenvalues for Hermitian matrices. However, few practical approach exists for the eigenanalysis of non-Hermintian ones, such as arising from modern power systems. The main difficulty lies in
Externí odkaz:
https://doaj.org/article/09a023524321422a94f6022856fa7db7
Autor:
Rajesh K. Malla, Hiroki Sukeno, Hongye Yu, Tzu-Chieh Wei, Andreas Weichselbaum, Robert M. Konik
Publikováno v:
Physical Review Research, Vol 6, Iss 4, p 043068 (2024)
In recent quantum algorithmic developments, a feedback-based approach has shown promise for preparing quantum many-body system ground states and solving combinatorial optimization problems. This method utilizes quantum Lyapunov control to iteratively
Externí odkaz:
https://doaj.org/article/14e6feccfeae473abb44ada61c07bfeb
Publikováno v:
Physical Review Physics Education Research, Vol 20, Iss 2, p 020126 (2024)
[This paper is part of the Focused Collection in Investigating and Improving Quantum Education through Research.] With the current growth in quantum information science and technology (QIST), there is an increasing need to prepare precollege students
Externí odkaz:
https://doaj.org/article/f0b23e71b0864b0f90cc44bef37594e5
Publikováno v:
PRX Quantum, Vol 5, Iss 3, p 030344 (2024)
Adaptive quantum circuits, which combine local unitary gates, midcircuit measurements, and feedforward operations, have recently emerged as a promising avenue for efficient state preparation, particularly on near-term quantum devices limited to shall
Externí odkaz:
https://doaj.org/article/143b6b23b35840258c3584df1acfe9ef
Autor:
Yin-He Jian, Chih-Chun Wang, Chi-Wai Chow, Wahyu Hendra Gunawan, Tzu-Chieh Wei, Yang Liu, Chien-Hung Yeh
Publikováno v:
IEEE Photonics Journal, Vol 15, Iss 4, Pp 1-8 (2023)
We put forward and demonstrate a steerable optical beam visible light communication (VLC) system combining orthogonal frequency division multiplexing (OFDM) and non-orthogonal multiple access (NOMA) schemes, and utilizing a spatial light modulator (S
Externí odkaz:
https://doaj.org/article/b5077a1a9245488cbc5d20583200900b
Autor:
Yifan Zhou, Zefan Tang, Nima Nikmehr, Pouya Babahajiani, Fei Feng, Tzu-Chieh Wei, Honghao Zheng, Peng Zhang
Publikováno v:
iEnergy, Vol 1, Iss 2, Pp 170-187 (2022)
Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today’s power systems are suffering from ev
Externí odkaz:
https://doaj.org/article/fdd8269cdc974d85aa89ee23055c653e
Publikováno v:
Physical Review Research, Vol 5, Iss 1, p 013183 (2023)
Quantum computers have the potential to efficiently simulate large-scale quantum systems for which classical approaches are bound to fail. Even though several existing quantum devices now feature total qubit numbers of more than 100, their applicabil
Externí odkaz:
https://doaj.org/article/370bb5502f0f4677a0ad5a7b63ef66c4
Publikováno v:
Physical Review Research, Vol 4, Iss 3, p 033045 (2022)
Quantum algorithms have been successfully applied in quantum chemistry to obtain the ground-state energy of small molecules. Although accurate near the equilibrium geometry, the results can become unreliable when the chemical bonds are broken at larg
Externí odkaz:
https://doaj.org/article/209f0da6d47c42fcaefcd545e56df5c4
Publikováno v:
Physical Review Research, Vol 4, Iss 1, p 013231 (2022)
This paper presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. The proposed quantum archite
Externí odkaz:
https://doaj.org/article/1c4bbd6c5d0e4adf8f7d3c17e29c51bd
Autor:
Sau Lan Wu, Shaojun Sun, Wen Guan, Chen Zhou, Jay Chan, Chi Lung Cheng, Tuan Pham, Yan Qian, Alex Zeng Wang, Rui Zhang, Miron Livny, Jennifer Glick, Panagiotis Kl. Barkoutsos, Stefan Woerner, Ivano Tavernelli, Federico Carminati, Alberto Di Meglio, Andy C. Y. Li, Joseph Lykken, Panagiotis Spentzouris, Samuel Yen-Chi Chen, Shinjae Yoo, Tzu-Chieh Wei
Publikováno v:
Physical Review Research, Vol 3, Iss 3, p 033221 (2021)
Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in high energy physics by offering computational speedups. In this study, we employ a support vector machine with a quantum kernel es
Externí odkaz:
https://doaj.org/article/47e6843f125a47ed8b9a8f9972bb4712