4,453 research outputs found
Programming multi-level quantum gates in disordered computing reservoirs via machine learning and TensorFlow
Novel machine learning computational tools open new perspectives for quantum
information systems. Here we adopt the open-source programming library
TensorFlow to design multi-level quantum gates including a computing reservoir
represented by a random unitary matrix. In optics, the reservoir is a
disordered medium or a multi-modal fiber. We show that trainable operators at
the input and the readout enable one to realize multi-level gates. We study
various qudit gates, including the scaling properties of the algorithms with
the size of the reservoir. Despite an initial low slop learning stage,
TensorFlow turns out to be an extremely versatile resource for designing gates
with complex media, including different models that use spatial light
modulators with quantized modulation levels.Comment: Added a new section and a new figure about implementation of the
gates by a single spatial light modulator. 9 pages and 4 figure
Single-image Tomography: 3D Volumes from 2D Cranial X-Rays
As many different 3D volumes could produce the same 2D x-ray image, inverting
this process is challenging. We show that recent deep learning-based
convolutional neural networks can solve this task. As the main challenge in
learning is the sheer amount of data created when extending the 2D image into a
3D volume, we suggest firstly to learn a coarse, fixed-resolution volume which
is then fused in a second step with the input x-ray into a high-resolution
volume. To train and validate our approach we introduce a new dataset that
comprises of close to half a million computer-simulated 2D x-ray images of 3D
volumes scanned from 175 mammalian species. Applications of our approach
include stereoscopic rendering of legacy x-ray images, re-rendering of x-rays
including changes of illumination, view pose or geometry. Our evaluation
includes comparison to previous tomography work, previous learning methods
using our data, a user study and application to a set of real x-rays
Solving Inverse Obstacle Scattering Problem with Latent Surface Representations
We propose a novel iterative numerical method to solve the three-dimensional
inverse obstacle scattering problem of recovering the shape of the obstacle
from far-field measurements. To address the inherent ill-posed nature of the
inverse problem, we advocate the use of a trained latent representation of
surfaces as the generative prior. This prior enjoys excellent expressivity
within the given class of shapes, and meanwhile, the latent dimensionality is
low, which greatly facilitates the computation. Thus, the admissible manifold
of surfaces is realistic and the resulting optimization problem is less
ill-posed. We employ the shape derivative to evolve the latent surface
representation, by minimizing the loss, and we provide a local convergence
analysis of a gradient descent type algorithm to a stationary point of the
loss. We present several numerical examples, including also backscattered and
phaseless data, to showcase the effectiveness of the proposed algorithm
- …