9,884 research outputs found
Two-axis-twisting spin squeezing by multi-pass quantum erasure
Many-body entangled states are key elements in quantum information science
and quantum metrology. One important problem in establishing a high degree of
many-body entanglement using optical techniques is the leakage of the system
information via the light that creates such entanglement. We propose an
all-optical interference-based approach to erase this information. Unwanted
atom-light entanglement can be removed by destructive interference of three or
more successive atom-light interactions, with only the desired effective
atom-atom interaction left. This quantum erasure protocol allows implementation
of Heisenberg-limited spin squeezing using coherent light and a cold or warm
atomic ensemble. Calculations show that significant improvement in the
squeezing exceeding 10 dB is obtained compared to previous methods, and
substantial spin squeezing is attainable even under moderate experimental
conditions. Our method enables the efficient creation of many-body entangled
states with simple setups, and thus is promising for advancing technologies in
quantum metrology and quantum information processing.Comment: 10 pages, 4 figures. We have improved the presentation and added a
new section, in which we have generalized the scheme from a three-pass scheme
to multi-pass schem
A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction
Magnetic resonance imaging (MRI) plays an important role in modern medical
diagnostic but suffers from prolonged scan time. Current deep learning methods
for undersampled MRI reconstruction exhibit good performance in image
de-aliasing which can be tailored to the specific kspace undersampling
scenario. But it is very troublesome to configure different deep networks when
the sampling setting changes. In this work, we propose a deep plug-and-play
method for undersampled MRI reconstruction, which effectively adapts to
different sampling settings. Specifically, the image de-aliasing prior is first
learned by a deep denoiser trained to remove general white Gaussian noise from
synthetic data. Then the learned deep denoiser is plugged into an iterative
algorithm for image reconstruction. Results on in vivo data demonstrate that
the proposed method provides nice and robust accelerated image reconstruction
performance under different undersampling patterns and sampling rates, both
visually and quantitatively.Comment: 5 pages, 3 figure
FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer
Federated Learning (FL) has been widely concerned for it enables
decentralized learning while ensuring data privacy. However, most existing
methods unrealistically assume that the classes encountered by local clients
are fixed over time. After learning new classes, this assumption will make the
model's catastrophic forgetting of old classes significantly severe. Moreover,
due to the limitation of communication cost, it is challenging to use
large-scale models in FL, which will affect the prediction accuracy. To address
these challenges, we propose a novel framework, Federated Enhanced Transformer
(FedET), which simultaneously achieves high accuracy and low communication
cost. Specifically, FedET uses Enhancer, a tiny module, to absorb and
communicate new knowledge, and applies pre-trained Transformers combined with
different Enhancers to ensure high precision on various tasks. To address local
forgetting caused by new classes of new tasks and global forgetting brought by
non-i.i.d (non-independent and identically distributed) class imbalance across
different local clients, we proposed an Enhancer distillation method to modify
the imbalance between old and new knowledge and repair the non-i.i.d. problem.
Experimental results demonstrate that FedET's average accuracy on
representative benchmark datasets is 14.1% higher than the state-of-the-art
method, while FedET saves 90% of the communication cost compared to the
previous method.Comment: Accepted by 2023 International Joint Conference on Artificial
Intelligence (IJCAI2023
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