Deep Photonic Reservoir Computer for Speech Recognition.

Speech recognition is a critical task in the field of artificial intelligence (AI) and has witnessed remarkable advancements thanks to large and complex neural networks, whose training process typically requires massive amounts of labeled data and computationally intensive operations. An alternative paradigm, reservoir computing (RC), is energy efficient and is well adapted to implementation in physical substrates, but exhibits limitations in performance when compared with more resource-intensive machine learning algorithms. In this work, we address this challenge by investigating different architectures of interconnected reservoirs, all falling under the umbrella of deep RC (DRC).

Integrated programmable spectral filter for frequency-multiplexed neuromorphic computers.

Artificial neural networks (ANN) are a groundbreaking technology massively employed in a plethora of fields. Currently, ANNs are mostly implemented through electronic digital computers, but analog photonic implementations are very interesting mainly because of low power consumption and high bandwidth. We recently demonstrated a photonic neuromorphic computing system based on frequency multiplexing that executes ANNs algorithms as reservoir computing and Extreme Learning Machines.

High speed human action recognition using a photonic reservoir computer.

The recognition of human actions in videos is one of the most active research fields in computer vision. The canonical approach consists in a more or less complex preprocessing stages of the raw video data, followed by a relatively simple classification algorithm. Here we address recognition of human actions using the reservoir computing algorithm, which allows us to focus on the classifier stage.

Photonic reservoir computer based on frequency multiplexing.

Reservoir computing is a brain-inspired approach for information processing, well suited to analog implementations. We report a photonic implementation of a reservoir computer that exploits frequency domain multiplexing to encode neuron states. The system processes 25 comb lines simultaneously (i.

Photonic extreme learning machine based on frequency multiplexing.

The optical domain is a promising field for the physical implementation of neural networks, due to the speed and parallelism of optics. Extreme learning machines (ELMs) are feed-forward neural networks in which only output weights are trained, while internal connections are randomly selected and left untrained. Here we report on a photonic ELM based on a frequency-multiplexed fiber setup.

Photonic Reservoir Computer with Output Expansion for Unsupervized Parameter Drift Compensation.

We present a method to improve the performance of a reservoir computer by keeping the reservoir fixed and increasing the number of output neurons. The additional neurons are nonlinear functions, typically chosen randomly, of the reservoir neurons. We demonstrate the interest of this expanded output layer on an experimental opto-electronic system subject to slow parameter drift which results in loss of performance.

Resource efficient single photon source based on active frequency multiplexing.

We propose a new, to the best of our knowledge, single photon source based on the principle of active multiplexing of heralded single photons, which, unlike previously reported architecture, requires a limited amount of physical resources. We discuss both its feasibility and the purity and indistinguishability of single photons as a function of the key parameters of a possible implementation.

Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography.

Using the machine learning approach known as reservoir computing, it is possible to train one dynamical system to emulate another. We show that such trained reservoir computers reproduce the properties of the attractor of the chaotic system sufficiently well to exhibit chaos synchronization. That is, the trained reservoir computer, weakly driven by the chaotic system, will synchronize with the chaotic system.

Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalization.

Reservoir computing is a bioinspired computing paradigm for processing time-dependent signals. The performance of its analog implementation is comparable to other state-of-the-art algorithms for tasks such as speech recognition or chaotic time series prediction, but these are often constrained by the offline training methods commonly employed. Here, we investigated the online learning approach by training an optoelectronic reservoir computer using a simple gradient descent algorithm, programmed on a field-programmable gate array chip.

Embodiment of Learning in Electro-Optical Signal Processors.

Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing. In particular, it has recently been demonstrated, using the artificial intelligence algorithm known as reservoir computing, that photonic implementations of such systems solve complex tasks such as speech recognition. Here, we show how the backpropagation algorithm can be physically implemented on the same electro-optical delay-coupled architecture used for computation with only minor changes to the original design.

Measuring the nonlinear refractive index of graphene using the optical Kerr effect method.

By means of the ultrafast optical Kerr effect method coupled to optical heterodyne detection (OHD-OKE), we characterize the third-order nonlinear response of graphene and compare it to experimental values obtained by the Z-scan method on the same samples. From these measurements, we estimate a negative nonlinear refractive index for monolayer graphene, n=-1.1×10  m/W.

Fully analogue photonic reservoir computer.

Introduced a decade ago, reservoir computing is an efficient approach for signal processing. State of the art capabilities have already been demonstrated with both computer simulations and physical implementations. If photonic reservoir computing appears to be promising a solution for ultrafast nontrivial computing, all the implementations presented up to now require digital pre or post processing, which prevents them from exploiting their full potential, in particular in terms of processing speed.

All-optical reservoir computer based on saturation of absorption.

Reservoir computing is a new bio-inspired computation paradigm. It exploits a dynamical system driven by a time-dependent input to carry out computation. For efficient information processing, only a few parameters of the reservoir needs to be tuned, which makes it a promising framework for hardware implementation.

Supercontinuum generation in hydrogenated amorphous silicon waveguides at telecommunication wavelengths.

We report supercontinuum (SC) generation centered on the telecommunication C-band (1550 nm) in CMOS compatible hydrogenated amorphous silicon waveguides. A broadening of more than 550 nm is obtained in 1cm long waveguides of different widths using as pump picosecond pulses with on chip peak power as low as 4 W.

Secure and robust transmission and verification of unknown quantum states in Minkowski space.

An important class of cryptographic applications of relativistic quantum information work as follows. B generates a random qudit and supplies it to A at point P. A is supposed to transmit it at near light speed c to to one of a number of possible pairwise spacelike separated points Q1, …, Qn.

Silicon-on-insulator integrated source of polarization-entangled photons.

We report the experimental generation of polarization-entangled photons at telecommunication wavelengths using spontaneous four-wave mixing in silicon-on-insulator wire waveguides. The key component is a 2D coupler that transforms path entanglement into polarization entanglement at the output of the device. Using quantum state tomography we find that the produced state has fidelity 88% with a pure nonmaximally entangled state.

Mean-field theory of echo state networks.

Dynamical systems driven by strong external signals are ubiquitous in nature and engineering. Here we study "echo state networks," networks of a large number of randomly connected nodes, which represent a simple model of a neural network, and have important applications in machine learning. We develop a mean-field theory of echo state networks.

All-optical reservoir computing.

Reservoir Computing is a novel computing paradigm that uses a nonlinear recurrent dynamical system to carry out information processing. Recent electronic and optoelectronic Reservoir Computers based on an architecture with a single nonlinear node and a delay loop have shown performance on standardized tasks comparable to state-of-the-art digital implementations. Here we report an all-optical implementation of a Reservoir Computer, made of off-the-shelf components for optical telecommunications.

Information processing capacity of dynamical systems.

Many dynamical systems, both natural and artificial, are stimulated by time dependent external signals, somehow processing the information contained therein. We demonstrate how to quantify the different modes in which information can be processed by such systems and combine them to define the computational capacity of a dynamical system. This is bounded by the number of linearly independent state variables of the dynamical system, equaling it if the system obeys the fading memory condition.

Z-scan measurement of the nonlinear refractive index of graphene.

Under strong laser illumination, few-layer graphene exhibits both a transmittance increase due to saturable absorption and a nonlinear phase shift. Here, we unambiguously distinguish these two nonlinear optical effects and identify both real and imaginary parts of the complex nonlinear refractive index of graphene. We show that graphene possesses a giant nonlinear refractive index n(2)≃10(-7) cm(2) W(-1), almost 9 orders of magnitude larger than bulk dielectrics.

Randomness versus nonlocality and entanglement.

The outcomes obtained in Bell tests involving two-outcome measurements on two subsystems can, in principle, generate up to 2 bits of randomness. However, the maximal violation of the Clauser-Horne-Shimony-Holt inequality guarantees the generation of only 1.23 bits of randomness.

On-chip parametric amplification with 26.5 dB gain at telecommunication wavelengths using CMOS-compatible hydrogenated amorphous silicon waveguides.

We present what we believe to be the first study of parametric amplification in hydrogenated amorphous silicon waveguides. Broadband on/off amplification up to 26.5 dB at telecom wavelength is reported.

The first peptides: the evolutionary transition between prebiotic amino acids and early proteins.

The issues we attempt to tackle here are what the first peptides did look like when they emerged on the primitive earth, and what simple catalytic activities they fulfilled. We conjecture that the early functional peptides were short (3-8 amino acids long), were made of those amino acids, Gly, Ala, Val and Asp, that are abundantly produced in many prebiotic synthesis experiments and observed in meteorites, and that the neutralization of Asp's negative charge is achieved by metal ions. We further assume that some traces of these prebiotic peptides still exist, in the form of active sites in present-day proteins.

Uncertainty relation for the discrete Fourier transform.

We derive an uncertainty relation for two unitary operators which obey a commutation relation of the form UV=e(i phi) VU. Its most important application is to constrain how much a quantum state can be localized simultaneously in two mutually unbiased bases related by a discrete fourier transform. It provides an uncertainty relation which smoothly interpolates between the well-known cases of the Pauli operators in two dimensions and the continuous variables position and momentum.

Spontaneous growth of Raman Stokes and anti-Stokes waves in fibers.

Using the quantum theory of light, we derive general analytical expressions of Stokes and anti-Stokes spectral photon-flux densities that are spontaneously generated by a single monochromatic pump wave propagating in a single-mode optical fiber. We validate our results by comparing them with experimental data. Limiting cases corresponding to interesting physical situations are discussed.