Zobrazeno 1 - 10
of 6 586
pro vyhledávání: '"Hong,Ye"'
Autor:
Shen, Yizhi, Buzali, Alex, Hu, Hong-Ye, Klymko, Katherine, Camps, Daan, Yelin, Susanne F., Van Beeumen, Roel
Quantum algorithms exploiting real-time evolution under a target Hamiltonian have demonstrated remarkable efficiency in extracting key spectral information. However, the broader potential of these methods, particularly beyond ground state calculation
Externí odkaz:
http://arxiv.org/abs/2409.13691
Quantum machine learning QML algorithms promise to deliver near-term, applicable quantum computation on noisy, intermediate-scale systems. While most of these algorithms leverage quantum circuits for generic applications, a recent set of proposals, c
Externí odkaz:
http://arxiv.org/abs/2408.14697
Spin-1/2 Heisenberg antiferromagnetic chains are excellent one-dimensional platforms for exploring quantum magnetic states and quasiparticle fractionalization. Understanding its quantum magnetism and quasiparticle excitation at the atomic scale is cr
Externí odkaz:
http://arxiv.org/abs/2408.08801
The manifold hypothesis says that natural high-dimensional data is actually supported on or around a low-dimensional manifold. Recent success of statistical and learning-based methods empirically supports this hypothesis, due to outperforming classic
Externí odkaz:
http://arxiv.org/abs/2408.06996
Autor:
Kornjača, Milan, Hu, Hong-Ye, Zhao, Chen, Wurtz, Jonathan, Weinberg, Phillip, Hamdan, Majd, Zhdanov, Andrii, Cantu, Sergio H., Zhou, Hengyun, Bravo, Rodrigo Araiza, Bagnall, Kevin, Basham, James I., Campo, Joseph, Choukri, Adam, DeAngelo, Robert, Frederick, Paige, Haines, David, Hammett, Julian, Hsu, Ning, Hu, Ming-Guang, Huber, Florian, Jepsen, Paul Niklas, Jia, Ningyuan, Karolyshyn, Thomas, Kwon, Minho, Long, John, Lopatin, Jonathan, Lukin, Alexander, Macrì, Tommaso, Marković, Ognjen, Martínez-Martínez, Luis A., Meng, Xianmei, Ostroumov, Evgeny, Paquette, David, Robinson, John, Rodriguez, Pedro Sales, Singh, Anshuman, Sinha, Nandan, Thoreen, Henry, Wan, Noel, Waxman-Lenz, Daniel, Wong, Tak, Wu, Kai-Hsin, Lopes, Pedro L. S., Boger, Yuval, Gemelke, Nathan, Kitagawa, Takuya, Keesling, Alexander, Gao, Xun, Bylinskii, Alexei, Yelin, Susanne F., Liu, Fangli, Wang, Sheng-Tao
Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant res
Externí odkaz:
http://arxiv.org/abs/2407.02553
Autor:
Zhou, Hengyun, Zhao, Chen, Cain, Madelyn, Bluvstein, Dolev, Duckering, Casey, Hu, Hong-Ye, Wang, Sheng-Tao, Kubica, Aleksander, Lukin, Mikhail D.
Fast, reliable logical operations are essential for the realization of useful quantum computers, as they are required to implement practical quantum algorithms at large scale. By redundantly encoding logical qubits into many physical qubits and using
Externí odkaz:
http://arxiv.org/abs/2406.17653
Autor:
Tan, Hong Ye, Cai, Ziruo, Pereyra, Marcelo, Mukherjee, Subhadip, Tang, Junqi, Schönlieb, Carola-Bibiane
Imaging is a standard example of an inverse problem, where the task of reconstructing a ground truth from a noisy measurement is ill-posed. Recent state-of-the-art approaches for imaging use deep learning, spearheaded by unrolled and end-to-end model
Externí odkaz:
http://arxiv.org/abs/2404.05445
Autor:
Evert, Bram, Izquierdo, Zoe Gonzalez, Sud, James, Hu, Hong-Ye, Grabbe, Shon, Rieffel, Eleanor G., Reagor, Matthew J., Wang, Zhihui
Theoretically understanding and experimentally characterizing and modifying the underlying Hamiltonian of a quantum system is of utmost importance in achieving high-fidelity quantum gates for quantum computing. In this work, we explore the use of dyn
Externí odkaz:
http://arxiv.org/abs/2403.07836
Autor:
Hu, Hong-Ye, Gu, Andi, Majumder, Swarnadeep, Ren, Hang, Zhang, Yipei, Wang, Derek S., You, Yi-Zhuang, Minev, Zlatko, Yelin, Susanne F., Seif, Alireza
Extracting information efficiently from quantum systems is a major component of quantum information processing tasks. Randomized measurements, or classical shadows, enable predicting many properties of arbitrary quantum states using few measurements.
Externí odkaz:
http://arxiv.org/abs/2402.17911
Diffusion probabilistic models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data, for instance, for computer vision, audio, natural language processing, or biomolecule generation. Here, we
Externí odkaz:
http://arxiv.org/abs/2402.12242