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pro vyhledávání: '"Assad, A"'
We present a novel probabilistic approach for generating multi-fidelity data while accounting for errors inherent in both low- and high-fidelity data. In this approach a graph Laplacian constructed from the low-fidelity data is used to define a multi
Externí odkaz:
http://arxiv.org/abs/2409.08211
Autor:
Shajilal, Biveen, Conlon, Lorcán O., Walsh, Angus, Tserkis, Spyros, Zhao, Jie, Janousek, Jiri, Assad, Syed, Lam, Ping Koy
Gaussian channel simulation is an essential paradigm in understanding the evolution of bosonic quantum states. It allows us to investigate how such states are influenced by the environment and how they transmit quantum information. This makes it an e
Externí odkaz:
http://arxiv.org/abs/2408.08667
Autor:
Conlon, Lorcan, Koh, Jin Ming, Shajilal, Biveen, Sidhu, Jasminder, Lam, Ping Koy, Assad, Syed M.
One of the fundamental tenets of quantum mechanics is that non-orthogonal states cannot be distinguished perfectly. When distinguishing multiple copies of a mixed quantum state, a collective measurement, which generates entanglement between the diffe
Externí odkaz:
http://arxiv.org/abs/2408.06678
Publikováno v:
Physical Review Research 6, 033315 (2024)
Measurement estimation bounds for local quantum multiparameter estimation, which provide lower bounds on possible measurement uncertainties, have so far been formulated in two ways: by extending the classical Cram\'er--Rao bound (e.g., the quantum Cr
Externí odkaz:
http://arxiv.org/abs/2407.12466
Addition of photons to coherent states is shown to produce effects that display remarkable similarities with cubic phase shifts acting on the vacuum state, with recorded fidelities in excess of 90 percent. The strength of the cubic interaction is fou
Externí odkaz:
http://arxiv.org/abs/2407.12265
Autor:
Dasgupta, Agnimitra, Ramaswamy, Harisankar, Murgoitio-Esandi, Javier, Foo, Ken, Li, Runze, Zhou, Qifa, Kennedy, Brendan, Oberai, Assad
We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a specimen's spatially varying material properties from noisy measurement
Externí odkaz:
http://arxiv.org/abs/2406.13154
Generative modeling has drawn much attention in creative and scientific data generation tasks. Score-based Diffusion Models, a type of generative model that iteratively learns to denoise data, have shown state-of-the-art results on tasks such as imag
Externí odkaz:
http://arxiv.org/abs/2405.11738
In multi-parameter quantum metrology, the resource of entanglement can lead to an increase in efficiency of the estimation process. Entanglement can be used in the state preparation stage, or the measurement stage, or both, to harness this advantage;
Externí odkaz:
http://arxiv.org/abs/2405.09622
Autor:
Zo, Wan, Bilash, Bohdan, Lee, Donghwa, Kim, Yosep, Lim, Hyang-Tag, Oh, Kyunghwan, Assad, Syed M., Kim, Yong-Su
Entanglement shared between distant parties is a key resource in quantum networks. However, photon losses in quantum channels significantly reduce the success probability of entanglement sharing, which scales quadratically with the channel transmissi
Externí odkaz:
http://arxiv.org/abs/2405.03951
In microservice applications, ensuring resilience during database or service disruptions constitutes a significant challenge. While several tools address resilience testing for service failures, there is a notable gap in tools specifically designed f
Externí odkaz:
http://arxiv.org/abs/2404.01886