84 research outputs found
All-optical Reservoir Computing
Reservoir Computing is a novel computing paradigm which 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. It uses the saturation of a
semiconductor optical amplifier as nonlinearity. The present work shows that,
within the Reservoir Computing paradigm, all-optical computing with
state-of-the-art performance is possible
Direct coupling of nonlinear integrated cavities for all-optical reservoir computing
We consider theoretically a network of directly coupled optical microcavities
to implement a space-multiplexed optical neural network in an integrated
nanophotonic circuit. Nonlinear photonic network integrations based on direct
coupling ensures a highly dense integration, reducing the chip footprint by
several orders of magnitude compared to other implementations. Different
nonlinear effects inherent to such microcavities are studied when used for
realizing an all-optical autonomous computing substrate, here based on the
reservoir computing concept. We provide an in-depth analysis of the impact of
basic microcavity parameters on computational metrics of the system, namely,
the dimensionality and the consistency. Importantly, we find that differences
between frequencies and bandwidths of supermodes formed by the direct coupling
is the determining factor of the reservoir's dimensionality and its
scalability. The network's dimensionality can be improved with
frequency-shifting nonlinear effects such as the Kerr effect, while two-photon
absorption has an opposite effect. Finally, we demonstrate in simulation that
the proposed reservoir is capable of solving the Mackey-Glass prediction and
the optical signal recovery tasks at GHz timescale
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
Silicon photonics for neuromorphic information processing
We present our latest results on silicon photonics neuromorphic information processing based a.o. on techniques like reservoir computing. We will discuss aspects like scalability, novel architectures for enhanced power efficiency, as well as all-optical readout. Additionally, we will touch upon new machine learning techniques to operate these integrated readouts. Finally, we will show how these systems can be used for high-speed low-power information processing for applications like recognition of biological cells
High performance photonic reservoir computer based on a coherently driven passive cavity
Reservoir computing is a recent bio-inspired approach for processing
time-dependent signals. It has enabled a breakthrough in analog information
processing, with several experiments, both electronic and optical,
demonstrating state-of-the-art performances for hard tasks such as speech
recognition, time series prediction and nonlinear channel equalization. A
proof-of-principle experiment using a linear optical circuit on a photonic chip
to process digital signals was recently reported. Here we present a photonic
implementation of a reservoir computer based on a coherently driven passive
fiber cavity processing analog signals. Our experiment has error rate as low or
lower than previous experiments on a wide variety of tasks, and also has lower
power consumption. Furthermore, the analytical model describing our experiment
is also of interest, as it constitutes a very simple high performance reservoir
computer algorithm. The present experiment, given its good performances, low
energy consumption and conceptual simplicity, confirms the great potential of
photonic reservoir computing for information processing applications ranging
from artificial intelligence to telecommunicationsComment: non
Low-loss photonic reservoir computing with multimode photonic integrated circuits
Abstract We present a numerical study of a passive integrated photonics reservoir computing platform based on multimodal Y-junctions. We propose a novel design of this junction where the level of adiabaticity is carefully tailored to capture the radiation loss in higher-order modes, while at the same time providing additional mode mixing that increases the richness of the reservoir dynamics. With this design, we report an overall average combination efficiency of 61% compared to the standard 50% for the single-mode case. We demonstrate that with this design, much more power is able to reach the distant nodes of the reservoir, leading to increased scaling prospects. We use the example of a header recognition task to confirm that such a reservoir can be used for bit-level processing tasks. The design itself is CMOS-compatible and can be fabricated through the known standard fabrication procedures
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