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pro vyhledávání: '"Koo, Kevin"'
A high-brightness entangled photon pair (HBEPP) source is essential for conducting entanglement-based quantum key distribution (QKD) between a satellite and a ground station. While an ultrabright source can overcome significant losses in satellite-ba
Externí odkaz:
http://arxiv.org/abs/2408.14768
Adiabatic quantum annealers encounter scalability challenges due to exponentially fast diminishing energy gaps between ground and excited states with qubit-count increase. This introduces errors in identifying ground states compounded by a thermal no
Externí odkaz:
http://arxiv.org/abs/2405.12594
Autor:
Kim, Kyungmin, Lim, Sumin, Shin, Kyujin, Lee, Gwonhak, Jung, Yousung, Kyoung, Woomin, Rhee, June-Koo Kevin, Rhee, Young Min
The realization of quantum advantage with noisy-intermediate-scale quantum (NISQ) machines has become one of the major challenges in computational sciences. Maintaining coherence of a physical system with more than ten qubits is a critical challenge
Externí odkaz:
http://arxiv.org/abs/2310.07650
In quantum machine learning, algorithms with parameterized quantum circuits (PQC) based on a hardware-efficient ansatz (HEA) offer the potential for speed-ups over traditional classical algorithms. While much attention has been devoted to supervised
Externí odkaz:
http://arxiv.org/abs/2309.04465
Autor:
Zhao, Luning, Goings, Joshua, Wright, Kenneth, Nguyen, Jason, Kim, Jungsang, Johri, Sonika, Shin, Kyujin, Kyoung, Woomin, Fuks, Johanna I., Rhee, June-Koo Kevin, Rhee, Young Min
Variational quantum eigensolvers (VQE) are among the most promising approaches for solving electronic structure problems on near-term quantum computers. A critical challenge for VQE in practice is that one needs to strike a balance between the expres
Externí odkaz:
http://arxiv.org/abs/2212.02482
Publikováno v:
Sci Rep 13, 3288 (2023)
A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm tha
Externí odkaz:
http://arxiv.org/abs/2206.14507
Quantum neural networks are promising for a wide range of applications in the Noisy Intermediate-Scale Quantum era. As such, there is an increasing demand for automatic quantum neural architecture search. We tackle this challenge by designing a quant
Externí odkaz:
http://arxiv.org/abs/2206.14115
Autor:
Fitzpatrick, Kira J., Rohlf, Hayden J., Phillips, Grant, Macaulay, R. Bruce, Anderson, Will, Price, Rochelle, Wood, Caitlin, James, Ameh, Langhorne, Charlotte, te Brake, Bill, Gibson, Justine S., Koo, Kevin M.
Publikováno v:
In Talanta 1 September 2024 277
Autor:
Hocheol Lim, Doo Hyung Kang, Jeonghoon Kim, Aidan Pellow-Jarman, Shane McFarthing, Rowan Pellow-Jarman, Hyeon-Nae Jeon, Byungdu Oh, June-Koo Kevin Rhee, Kyoung Tai No
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Quantum computers offer significant potential for complex system analysis, yet their application in large systems is hindered by limitations such as qubit availability and quantum hardware noise. While the variational quantum eigensolver (VQ
Externí odkaz:
https://doaj.org/article/dfd166ae7f7e4143acc2967d81ac3730