21,675 research outputs found
Protecting quantum entanglement from leakage and qubit errors via repetitive parity measurements
Protecting quantum information from errors is essential for large-scale
quantum computation. Quantum error correction (QEC) encodes information in
entangled states of many qubits, and performs parity measurements to identify
errors without destroying the encoded information. However, traditional QEC
cannot handle leakage from the qubit computational space. Leakage affects
leading experimental platforms, based on trapped ions and superconducting
circuits, which use effective qubits within many-level physical systems. We
investigate how two-transmon entangled states evolve under repeated parity
measurements, and demonstrate the use of hidden Markov models to detect leakage
using only the record of parity measurement outcomes required for QEC. We show
the stabilization of Bell states over up to 26 parity measurements by
mitigating leakage using postselection, and correcting qubit errors using
Pauli-frame transformations. Our leakage identification method is
computationally efficient and thus compatible with real-time leakage tracking
and correction in larger quantum processors.Comment: 22 pages, 15 figure
A deep learning framework for quality assessment and restoration in video endoscopy
Endoscopy is a routine imaging technique used for both diagnosis and
minimally invasive surgical treatment. Artifacts such as motion blur, bubbles,
specular reflections, floating objects and pixel saturation impede the visual
interpretation and the automated analysis of endoscopy videos. Given the
widespread use of endoscopy in different clinical applications, we contend that
the robust and reliable identification of such artifacts and the automated
restoration of corrupted video frames is a fundamental medical imaging problem.
Existing state-of-the-art methods only deal with the detection and restoration
of selected artifacts. However, typically endoscopy videos contain numerous
artifacts which motivates to establish a comprehensive solution.
We propose a fully automatic framework that can: 1) detect and classify six
different primary artifacts, 2) provide a quality score for each frame and 3)
restore mildly corrupted frames. To detect different artifacts our framework
exploits fast multi-scale, single stage convolutional neural network detector.
We introduce a quality metric to assess frame quality and predict image
restoration success. Generative adversarial networks with carefully chosen
regularization are finally used to restore corrupted frames.
Our detector yields the highest mean average precision (mAP at 5% threshold)
of 49.0 and the lowest computational time of 88 ms allowing for accurate
real-time processing. Our restoration models for blind deblurring, saturation
correction and inpainting demonstrate significant improvements over previous
methods. On a set of 10 test videos we show that our approach preserves an
average of 68.7% which is 25% more frames than that retained from the raw
videos.Comment: 14 page
Learning a Dilated Residual Network for SAR Image Despeckling
In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated convolutions, which can both enlarge the receptive field and
maintain the filter size and layer depth with a lightweight structure. In
addition, skip connections and residual learning strategy are added to the
despeckling model to maintain the image details and reduce the vanishing
gradient problem. Compared with the traditional despeckling methods, the
proposed method shows superior performance over the state-of-the-art methods on
both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table
- …