Zobrazeno 1 - 10
of 550
pro vyhledávání: '"Cai Zhenyu"'
Publikováno v:
Frontiers in Medicine, Vol 11 (2024)
BackgroundThe association between rheumatoid arthritis (RA) and osteoporotic fracture has garnered considerable attention; however, the causal relationships between diseases remain uncertain. Therefore, this study employed Mendelian randomization (MR
Externí odkaz:
https://doaj.org/article/3408d29c9bdd48c0a31a137003866bea
Autor:
Scruby, Thomas R., Cai, Zhenyu
We show how looped pipeline architectures - which use short-range shuttling of physical qubits to achieve a finite amount of non-local connectivity - can be used to efficiently implement the fault-tolerant non-Clifford gate between 2D surface codes d
Externí odkaz:
http://arxiv.org/abs/2412.12529
Non-Markovian noise, arising from the memory effect in the environment, poses substantial challenges to conventional quantum noise suppression protocols, including quantum error correction and mitigation. We introduce a channel representation for arb
Externí odkaz:
http://arxiv.org/abs/2412.11220
Autor:
Hanzo, Lajos, Babar, Zunaira, Cai, Zhenyu, Chandra, Daryus, Djordjevic, Ivan B., Koczor, Balint, Ng, Soon Xin, Razavi, Mohsen, Simeone, Osvaldo
The recent advances in quantum information processing, sensing and communications are surveyed with the objective of identifying the associated knowledge gaps and formulating a roadmap for their future evolution. Since the operation of quantum system
Externí odkaz:
http://arxiv.org/abs/2412.00987
Autor:
Alexeev, Yuri, Farag, Marwa H., Patti, Taylor L., Wolf, Mark E., Ares, Natalia, Aspuru-Guzik, Alán, Benjamin, Simon C., Cai, Zhenyu, Chandani, Zohim, Fedele, Federico, Harrigan, Nicholas, Kim, Jin-Sung, Kyoseva, Elica, Lietz, Justin G., Lubowe, Tom, McCaskey, Alexander, Melko, Roger G., Nakaji, Kouhei, Peruzzo, Alberto, Stanwyck, Sam, Tubman, Norm M., Wang, Hanrui, Costa, Timothy
Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including th
Externí odkaz:
http://arxiv.org/abs/2411.09131
In quantum learning tasks, quantum memory can offer exponential reductions in statistical complexity compared to any single-copy strategies, but this typically necessitates at least doubling the system size. We show that such exponential reductions c
Externí odkaz:
http://arxiv.org/abs/2410.17718
Autor:
Sun, Zhu, Boyd, Gregory, Cai, Zhenyu, Jnane, Hamza, Koczor, Balint, Meister, Richard, Minko, Romy, Pring, Benjamin, Benjamin, Simon C., Stamatopoulos, Nikitas
We explore the important task of applying a phase $exp(i f(x))$ to a computational basis state $\left| x \right>$. The closely related task of rotating a target qubit by an angle depending on $f(x)$ is also studied. Such operations are key in many qu
Externí odkaz:
http://arxiv.org/abs/2409.04587
We introduce a post-processing technique for classical shadow measurement data that enhances the precision of ground state estimation through high-dimensional subspace expansion; the dimensionality is only limited by the amount of classical post-proc
Externí odkaz:
http://arxiv.org/abs/2406.11533
Extracting classical information from quantum systems is of fundamental importance, and classical shadows allow us to extract a large amount of information using relatively few measurements. Conventional shadow estimators are unbiased and thus approa
Externí odkaz:
http://arxiv.org/abs/2402.09511
Quantum error mitigation is a key approach for extracting target state properties on state-of-the-art noisy machines and early fault-tolerant devices. Using the ideas from flag fault tolerance and virtual state purification, we develop the virtual ch
Externí odkaz:
http://arxiv.org/abs/2402.07866