151 research outputs found
Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network
Quantitative phase imaging (QPI) is a label-free computational imaging
technique used in various fields, including biology and medical research.
Modern QPI systems typically rely on digital processing using iterative
algorithms for phase retrieval and image reconstruction. Here, we report a
diffractive optical network trained to convert the phase information of input
objects positioned behind random diffusers into intensity variations at the
output plane, all-optically performing phase recovery and quantitative imaging
of phase objects completely hidden by unknown, random phase diffusers. This QPI
diffractive network is composed of successive diffractive layers, axially
spanning in total ~70 wavelengths; unlike existing digital image reconstruction
and phase retrieval methods, it forms an all-optical processor that does not
require external power beyond the illumination beam to complete its QPI
reconstruction at the speed of light propagation. This all-optical diffractive
processor can provide a low-power, high frame rate and compact alternative for
quantitative imaging of phase objects through random, unknown diffusers and can
operate at different parts of the electromagnetic spectrum for various
applications in biomedical imaging and sensing. The presented QPI diffractive
designs can be integrated onto the active area of standard CCD/CMOS-based image
sensors to convert an existing optical microscope into a diffractive QPI
microscope, performing phase recovery and image reconstruction on a chip
through light diffraction within passive structured layers.Comment: 27 Pages, 7 Figure
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
Ensemble learning of diffractive optical networks
A plethora of research advances have emerged in the fields of optics and
photonics that benefit from harnessing the power of machine learning.
Specifically, there has been a revival of interest in optical computing
hardware, due to its potential advantages for machine learning tasks in terms
of parallelization, power efficiency and computation speed. Diffractive Deep
Neural Networks (D2NNs) form such an optical computing framework, which
benefits from deep learning-based design of successive diffractive layers to
all-optically process information as the input light diffracts through these
passive layers. D2NNs have demonstrated success in various tasks, including
e.g., object classification, spectral-encoding of information, optical pulse
shaping and imaging, among others. Here, we significantly improve the inference
performance of diffractive optical networks using feature engineering and
ensemble learning. After independently training a total of 1252 D2NNs that were
diversely engineered with a variety of passive input filters, we applied a
pruning algorithm to select an optimized ensemble of D2NNs that collectively
improve their image classification accuracy. Through this pruning, we
numerically demonstrated that ensembles of N=14 and N=30 D2NNs achieve blind
testing accuracies of 61.14% and 62.13%, respectively, on the classification of
CIFAR-10 test images, providing an inference improvement of >16% compared to
the average performance of the individual D2NNs within each ensemble. These
results constitute the highest inference accuracies achieved to date by any
diffractive optical neural network design on the same dataset and might provide
a significant leapfrog to extend the application space of diffractive optical
image classification and machine vision systems.Comment: 22 Pages, 4 Figures, 1 Tabl
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