253,352 research outputs found
Effects of early dark energy on strong cluster lensing
We use the semi-analytic method developed by Fedeli et al. for computing
strong-lensing optical depths to study the statistics of gravitational arcs in
four dark-energy cosmologies. Specifically, we focus on models with early dark
energy and compare them to more conventional models. Merger trees are
constructed for the cluster population because strong cluster lensing is
amplified by factors of two to three during mergers. We find that the optical
depth for gravitational arcs in the early dark-energy models is increased by up
to a factor of about 3 compared to the other models because of the modified
dynamics of cluster formation. In particular, the probability for gravitational
arcs in high-redshift clusters is considerably increased, which may offer an
explanation for the unexpectedly high lensing efficiency of distant clusters.Comment: 10 pages, 9 figures, accepted for publication on A&
Frequency and temporal effects in linear optical quantum computing
Typically linear optical quantum computing (LOQC) models assume that all
input photons are completely indistinguishable. In practice there will
inevitably be non-idealities associated with the photons and the experimental
setup which will introduce a degree of distinguishability between photons. We
consider a non-deterministic optical controlled-NOT gate, a fundamental LOQC
gate, and examine the effect of temporal and spectral distinguishability on its
operation. We also consider the effect of utilizing non-ideal photon counters,
which have finite bandwidth and time response.Comment: 10 pages, 9 figures, replaced with published versio
Adiabatic evolution on a spatial-photonic Ising machine
Combinatorial optimization problems are crucial for widespread applications
but remain difficult to solve on a large scale with conventional hardware.
Novel optical platforms, known as coherent or photonic Ising machines, are
attracting considerable attention as accelerators on optimization tasks
formulable as Ising models. Annealing is a well-known technique based on
adiabatic evolution for finding optimal solutions in classical and quantum
systems made by atoms, electrons, or photons. Although various Ising machines
employ annealing in some form, adiabatic computing on optical settings has been
only partially investigated. Here, we realize the adiabatic evolution of
frustrated Ising models with 100 spins programmed by spatial light modulation.
We use holographic and optical control to change the spin couplings
adiabatically, and exploit experimental noise to explore the energy landscape.
Annealing enhances the convergence to the Ising ground state and allows to find
the problem solution with probability close to unity. Our results demonstrate a
photonic scheme for combinatorial optimization in analogy with adiabatic
quantum algorithms and enforced by optical vector-matrix multiplications and
scalable photonic technology.Comment: 9 pages, 4 figure
Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing
Large machine learning models based on Convolutional Neural Networks (CNNs)
with rapidly increasing number of parameters, trained with massive amounts of
data, are being deployed in a wide array of computer vision tasks from
self-driving cars to medical imaging. The insatiable demand for computing
resources required to train these models is fast outpacing the advancement of
classical computing hardware, and new frameworks including Optical Neural
Networks (ONNs) and quantum computing are being explored as future
alternatives.
In this work, we report a novel quantum computing based deep learning model,
the Quantum Optical Convolutional Neural Network (QOCNN), to alleviate the
computational bottleneck in future computer vision applications. Using the
popular MNIST dataset, we have benchmarked this new architecture against a
traditional CNN based on the seminal LeNet model. We have also compared the
performance with previously reported ONNs, namely the GridNet and ComplexNet,
as well as a Quantum Optical Neural Network (QONN) that we built by combining
the ComplexNet with quantum based sinusoidal nonlinearities. In essence, our
work extends the prior research on QONN by adding quantum convolution and
pooling layers preceding it.
We have evaluated all the models by determining their accuracies, confusion
matrices, Receiver Operating Characteristic (ROC) curves, and Matthews
Correlation Coefficients. The performance of the models were similar overall,
and the ROC curves indicated that the new QOCNN model is robust. Finally, we
estimated the gains in computational efficiencies from executing this novel
framework on a quantum computer. We conclude that switching to a quantum
computing based approach to deep learning may result in comparable accuracies
to classical models, while achieving unprecedented boosts in computational
performances and drastic reduction in power consumption.Comment: 9 pages, 6 figure
Computing optical flow in the primate visual system
Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We show how gradient models, a well-known class of motion algorithms, can be implemented within the magnocellular pathway of the primate's visual system. Our cooperative algorithm computes optical flow in two steps. In the first stage, assumed to be located in primary visual cortex, local motion is measured while spatial integration occurs in the second stage, assumed to be located in the middle temporal area (MT). The final optical flow is extracted in this second stage using population coding, such that the velocity is represented by the vector sum of neurons coding for motion in different directions. Our theory, relating the single-cell to the perceptual level, accounts for a number of psychophysical and electrophysiological observations and illusions
High-throughput optical neural networks based on temporal computing
An emerging generative artificial intelligence (AI) based on neural networks
starts to grow in popularity with a revolutionizing capability of creating new
and original content. As giant generative models with millions to billions of
parameters are developed, trained and maintained, a massive and
energy-efficient computing power is highly required. However, conventional
digital computers are struggling to keep up with the pace of the generative
model improvements. In this paper, we propose and demonstrate high-throughput
optical neural networks based on temporal computing. The core weighted
summation operation is realized with the use of high-speed electro-optic
modulation and low-speed balanced photodetection. The input data and weight are
encoded in a time sequence separately and loaded on an optical signal via two
electro-optic modulators sequentially. By precisely controlling the
synchronization time of the data and weight loading, the matrix multiplication
is performed. Followed by a balanced photodetector, the summation is conducted,
thanks to the electron accumulation of the inherent electronic integrator
circuit of the low-speed photodetector. Thus, the linear weighted summation
operation is implemented based on temporal computing in the optical domain.
With the proposed optical linear weighted summation, a fully-connected neural
network and convolutional neural network are realized. Thanks to the high-speed
feature of temporal computing, a high data throughput of the optical neural
network is experimentally demonstrated, and the weighting coefficients can be
specified on demand, which enables a strong programmability of the optical
neural network. By leveraging wavelength multiplexing technology, a scalable
optical neural network could be created with a massive computing power and
strong reconfigurability, which holds great potential for future giant AI
applications
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