2,049 research outputs found
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
Time-Delay Polaritonics
Non-linearity and finite signal propagation speeds are omnipresent in nature,
technologies, and real-world problems, where efficient ways of describing and
predicting the effects of these elements are in high demand. Advances in
engineering condensed matter systems, such as lattices of trapped condensates,
have enabled studies on non-linear effects in many-body systems where exchange
of particles between lattice nodes is effectively instantaneous. Here, we
demonstrate a regime of macroscopic matter-wave systems, in which ballistically
expanding condensates of microcavity exciton-polaritons act as picosecond,
microscale non-linear oscillators subject to time-delayed interaction. The ease
of optical control and readout of polariton condensates enables us to explore
the phase space of two interacting condensates up to macroscopic distances
highlighting its potential in extended configurations. We demonstrate
deterministic tuning of the coupled-condensate system between fixed point and
limit cycle regimes, which is fully reproduced by time-delayed coupled
equations of motion similar to the Lang-Kobayashi equation
Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch
We propose a new method for performing photonic circuit simulations based on the scatter matrix formalism. We leverage the popular deep-learning framework PyTorch to reimagine photonic circuits as sparsely connected complex-valued neural networks. This allows for highly parallel simulation of large photonic circuits on graphical processing units in time and frequency domain while all parameters of each individual component can easily be optimized with well-established machine learning algorithms such as backpropagation
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
An exact mathematical description of computation with transient spatiotemporal dynamics in a complex-valued neural network
We study a complex-valued neural network (cv-NN) with linear, time-delayed
interactions. We report the cv-NN displays sophisticated spatiotemporal
dynamics, including partially synchronized ``chimera'' states. We then use
these spatiotemporal dynamics, in combination with a nonlinear readout, for
computation. The cv-NN can instantiate dynamics-based logic gates, encode
short-term memories, and mediate secure message passing through a combination
of interactions and time delays. The computations in this system can be fully
described in an exact, closed-form mathematical expression. Finally, using
direct intracellular recordings of neurons in slices from neocortex, we
demonstrate that computations in the cv-NN are decodable by living biological
neurons. These results demonstrate that complex-valued linear systems can
perform sophisticated computations, while also being exactly solvable. Taken
together, these results open future avenues for design of highly adaptable,
bio-hybrid computing systems that can interface seamlessly with other neural
networks
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