Zobrazeno 1 - 10
of 49 246
pro vyhledávání: '"Kim, Yong‐In"'
The learner's ability to generate a hypothesis that closely approximates the target function is crucial in machine learning. Achieving this requires sufficient data; however, unauthorized access by an eavesdropping learner can lead to security risks.
Externí odkaz:
http://arxiv.org/abs/2501.00754
Autor:
Kim, Yong-Hoon, Lee, Ryong-Gyu
The continued miniaturization of semiconductor devices, represented by Moore's law, has reached the atomic scale limit, requiring nanoscale quantum mechanical effects to be included in device simulations without empirical parameters. For this purpose
Externí odkaz:
http://arxiv.org/abs/2411.11780
Autor:
Ding, Jixun K., Zhang, Emily Z., Wang, Wen O., Cookmeyer, Tessa, Moritz, Brian, Kim, Yong Baek, Devereaux, Thomas P.
In light of recent experimental data indicating a substantial thermal Hall effect in square lattice antiferromagnetic Mott insulators, we investigate whether a simple Mott insulator can sustain a finite thermal Hall effect. We verify that the answer
Externí odkaz:
http://arxiv.org/abs/2410.14863
Quantum entanglement serves as a foundational resource for various quantum technologies. In optical systems, entanglement distribution rely on the indistinguishability and spatial overlap of photons. Heralded schemes play a crucial role in ensuring t
Externí odkaz:
http://arxiv.org/abs/2409.16622
Quantum dynamics compilation is an important task for improving quantum simulation efficiency: It aims to synthesize multi-qubit target dynamics into a circuit consisting of as few elementary gates as possible. Compared to deterministic methods such
Externí odkaz:
http://arxiv.org/abs/2409.16346
Entangled photon sources are essential for quantum information applications, including quantum computation, quantum communication, and quantum metrology. Periodically poled (PP) crystals are commonly used to generate bright photon sources through qua
Externí odkaz:
http://arxiv.org/abs/2409.07673
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:
Park, Sangjoon, Wee, Chan Woo, Choi, Seo Hee, Kim, Kyung Hwan, Chang, Jee Suk, Yoon, Hong In, Lee, Ik Jae, Kim, Yong Bae, Cho, Jaeho, Keum, Ki Chang, Lee, Chang Geol, Byun, Hwa Kyung, Koom, Woong Sub
Accurate survival prediction in radiotherapy (RT) is critical for optimizing treatment decisions. This study developed and validated the RT-Surv framework, which integrates general-domain, open-source large language models (LLMs) to structure unstruc
Externí odkaz:
http://arxiv.org/abs/2408.05074
Publikováno v:
Phys. Rev. Lett. 133, 050605 (2024)
We propose a fault-tolerant quantum computation scheme in a measurement-based manner with finite-sized entangled resource states and encoded fusion scheme with linear optics. The encoded-fusion is an entangled measurement devised to enhance the fusio
Externí odkaz:
http://arxiv.org/abs/2408.01041
Dipolar-octupolar (DO) pyrochlore systems Ce$_2$(Zr,Sn,Hf)$_2$O$_7$ have garnered much attention as recent investigations suggest that they may stabilize a novel quantum spin ice (QSI), a quantum spin liquid (QSL) with an emergent $U(1)$ gauge field.
Externí odkaz:
http://arxiv.org/abs/2406.18650