12,743 research outputs found
Squeezed Light and Entangled Images from Four-Wave-Mixing in Hot Rubidium Vapor
Entangled multi-spatial-mode fields have interesting applications in quantum
information, such as parallel quantum information protocols, quantum computing,
and quantum imaging. We study the use of a nondegenerate four-wave mixing
process in rubidium vapor at 795 nm to demonstrate generation of
quantum-entangled images. Owing to the lack of an optical resonator cavity, the
four-wave mixing scheme generates inherently multi-spatial-mode output fields.
We have verified the presence of entanglement between the multi-mode beams by
analyzing the amplitude difference and the phase sum noise using a dual
homodyne detection scheme, measuring more than 4 dB of squeezing in both cases.
This paper will discuss the quantum properties of amplifiers based on
four-wave-mixing, along with the multi mode properties of such devices.Comment: 11 pages, 8 figures. SPIE Optics and Photonics 2008 proceeding (San
Diego, CA
Nanophotonic reservoir computing with photonic crystal cavities to generate periodic patterns
Reservoir computing (RC) is a technique in machine learning inspired by neural systems. RC has been used successfully to solve complex problems such as signal classification and signal generation. These systems are mainly implemented in software, and thereby they are limited in speed and power efficiency. Several optical and optoelectronic implementations have been demonstrated, in which the system has signals with an amplitude and phase. It is proven that these enrich the dynamics of the system, which is beneficial for the performance. In this paper, we introduce a novel optical architecture based on nanophotonic crystal cavities. This allows us to integrate many neurons on one chip, which, compared with other photonic solutions, closest resembles a classical neural network. Furthermore, the components are passive, which simplifies the design and reduces the power consumption. To assess the performance of this network, we train a photonic network to generate periodic patterns, using an alternative online learning rule called first-order reduced and corrected error. For this, we first train a classical hyperbolic tangent reservoir, but then we vary some of the properties to incorporate typical aspects of a photonics reservoir, such as the use of continuous-time versus discrete-time signals and the use of complex-valued versus real-valued signals. Then, the nanophotonic reservoir is simulated and we explore the role of relevant parameters such as the topology, the phases between the resonators, the number of nodes that are biased and the delay between the resonators. It is important that these parameters are chosen such that no strong self-oscillations occur. Finally, our results show that for a signal generation task a complex-valued, continuous-time nanophotonic reservoir outperforms a classical (i.e., discrete-time, real-valued) leaky hyperbolic tangent reservoir (normalized root-mean-square errors = 0.030 versus NRMSE = 0.127)
Progress of analog-hybrid computation
Review of fast analog/hybrid computer systems, integrated operational amplifiers, electronic mode-control switches, digital attenuators, and packaging technique
A simple circuit realization of the tent map
We present a very simple electronic implementation of the tent map, one of
the best-known discrete dynamical systems. This is achieved by using integrated
circuits and passive elements only. The experimental behavior of the tent map
electronic circuit is compared with its numerical simulation counterpart. We
find that the electronic circuit presents fixed points, periodicity, period
doubling, chaos and intermittency that match with high accuracy the
corresponding theoretical valuesComment: 6 pages, 6 figures, 10 references, published versio
Photonic reservoir computing: a new approach to optical information processing
Despite ever increasing computational power, recognition and classification problems remain challenging to solve. Recently advances have been made by the introduction of the new concept of reservoir computing. This is a methodology coming from the field of machine learning and neural networks and has been successfully used in several pattern classification problems, like speech and image recognition. The implementations have so far been in software, limiting their speed and power efficiency. Photonics could be an excellent platform for a hardware implementation of this concept because of its inherent parallelism and unique nonlinear behaviour. We propose using a network of coupled Semiconductor Optical Amplifiers (SOA) and show in simulation that it could be used as a reservoir by comparing it on a benchmark speech recognition task to conventional software implementations. In spite of several differences, they perform as good as or better than conventional implementations. Moreover, a photonic implementation offers the promise of massively parallel information processing with low power and high speed. We will also address the role phase plays on the reservoir performance
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Digital Predistortion in Large-Array Digital Beamforming Transmitters
In this article, we propose a novel digital predistortion (DPD) solution that
allows to considerably reduce the complexity resulting from linearizing a set
of power amplifiers (PAs) in single-user large-scale digital beamforming
transmitters. In contrast to current state-of-the art solutions that assume a
dedicated DPD per power amplifier, which is unfeasible in the context of large
antenna arrays, the proposed solution only requires a single DPD in order to
linearize an arbitrary number of power amplifiers. To this end, the proposed
DPD predistorts the signal at the input of the digital precoder based on
minimizing the nonlinear distortion of the combined signal at the intended
receiver direction. This is a desirable feature, since the resulting emissions
in other directions get partially diluted due to less coherent superposition.
With this approach, only a single DPD is required, yielding great complexity
and energy savings.Comment: 8 pages, Accepted for publication in Asilomar Conference on Signals,
Systems, and Computer
Nonlinearity Mitigation in WDM Systems: Models, Strategies, and Achievable Rates
After reviewing models and mitigation strategies for interchannel nonlinear
interference (NLI), we focus on the frequency-resolved logarithmic perturbation
model to study the coherence properties of NLI. Based on this study, we devise
an NLI mitigation strategy which exploits the synergic effect of phase and
polarization noise compensation (PPN) and subcarrier multiplexing with
symbol-rate optimization. This synergy persists even for high-order modulation
alphabets and Gaussian symbols. A particle method for the computation of the
resulting achievable information rate and spectral efficiency (SE) is presented
and employed to lower-bound the channel capacity. The dependence of the SE on
the link length, amplifier spacing, and presence or absence of inline
dispersion compensation is studied. Single-polarization and dual-polarization
scenarios with either independent or joint processing of the two polarizations
are considered. Numerical results show that, in links with ideal distributed
amplification, an SE gain of about 1 bit/s/Hz/polarization can be obtained (or,
in alternative, the system reach can be doubled at a given SE) with respect to
single-carrier systems without PPN mitigation. The gain is lower with lumped
amplification, increases with the number of spans, decreases with the span
length, and is further reduced by in-line dispersion compensation. For
instance, considering a dispersion-unmanaged link with lumped amplification and
an amplifier spacing of 60 km, the SE after 80 spans can be be increased from
4.5 to 4.8 bit/s/Hz/polarization, or the reach raised up to 100 spans (+25%)
for a fixed SE.Comment: Submitted to Journal of Lightwave Technolog
Time-domain analysis of RF and microwave autonomous circuits by vector fitting-based approach
This work presents a new method for the analysis of RF and microwave autonomous circuits directly in the time-domain, which is the most effective approach at simulation level to evaluate nonlinear phenomena. For RF and microwave autonomous circuits, time-domain simulations usually experiment convergence problems or numerical inaccuracies due to the presence of distributed elements, preventing de-facto their use. The proposed solution is based on the Vector Fitting algorithm applied directly at circuit level. A case study relative to a RF hybrid oscillator is presented for practical demonstration and evaluation of performance reliability of the proposed method
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