100 research outputs found
Complex extreme nonlinear waves: classical and quantum theory for new computing models
The historical role of nonlinear waves in developing the science of complexity, and also their physical feature of being a widespread paradigm in optics, establishes a bridge between two diverse and fundamental fields that can open an immeasurable number of new routes. In what follows, we present our most important results on nonlinear waves in classical and quantum nonlinear optics. About classical phenomenology, we lay the groundwork for establishing one uniform theory of dispersive shock waves, and for controlling complex nonlinear regimes through simple integer topological invariants. The second quantized field theory of optical propagation in nonlinear dispersive media allows us to perform numerical simulations of quantum solitons and the quantum nonlinear box problem. The complexity of light propagation in nonlinear media is here examined from all the main points of view: extreme phenomena, recurrence, control, modulation instability, and so forth. Such an analysis has a major, significant goal: answering the question can nonlinear waves do computation? For this purpose, our study towards the realization of an all-optical computer, able to do computation by implementing machine learning algorithms, is illustrated. The first all-optical realization of the Ising machine and the theoretical foundations of the random optical machine are here reported. We believe that this treatise is a fundamental study for the application of nonlinear waves to new computational techniques, disclosing new procedures to the control of extreme waves, and to the design of new quantum sources and non-classical state generators for future quantum technologies, also giving incredible insights about all-optical reservoir computing. Can nonlinear waves do computation? Our random optical machine draws the route for a positive answer to this question, substituting the randomness either with the uncertainty of quantum noise effects on light propagation or with the arbitrariness of classical, extremely nonlinear regimes, as similarly done by random projection methods and extreme learning machines
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
Sine-Gordon soliton as a model for Hawking radiation of moving black holes and quantum soliton evaporation
The intriguing connection between black holes' evaporation and physics of
solitons is opening novel roads to finding observable phenomena. It is known
from the inverse scattering transform that velocity is a fundamental parameter
in solitons theory. Taking this into account, the study of Haw\-king radiation
by a moving soliton gets a growing relevance. However, a theoretical context
for the description of this phenomenon is still lacking. Here, we adopt a
soliton geometrization technique to study the quantum emission of a moving
soliton in a one-dimensional model. Representing a black hole by the one
soliton solution of the sine-Gordon equation, we consider Haw\-king emission
spectra of a quantized massless scalar field on the soliton-induced metric. We
study the relation between the soliton velocity and the black hole temperature.
Our results address a new scenario in the detection of new physics in the
quantum gravity panorama.Comment: 8 pages, 4 figure
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, knownas 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 classical annealing
methods but enforced by optical vector-matrix multiplications and scalable photonic technology
Machine Learning Inverse Problem for Topological Photonics
Topological concepts open many new horizons for photonic devices, from
integrated optics to lasers. The complexity of large scale topological devices
asks for an effective solution of the inverse problem: how best to engineer the
topology for a specific application? We introduce a novel machine learning
approach to the topological inverse problem. We train a neural network system
with the band structure of the Aubry-Andre-Harper model and then adopt the
network for solving the inverse problem. Our application is able to identify
the parameters of a complex topological insulator in order to obtain protected
edge states at target frequencies. One challenging aspect is handling the
multivalued branches of the direct problem and discarding unphysical solutions.
We overcome this problem by adopting a self-consistent method to only select
physically relevant solutions. We demonstrate our technique in a realistic
topological laser design and by resorting to the widely available open-source
TensorFlow library. Our results are general and scalable to thousands of
topological components. This new inverse design technique based on machine
learning potentially extends the applications of topological photonics, for
example, to frequency combs, quantum sources, neuromorphic computing and
metrology
Optimization of a Hydrodynamic Computational Reservoir through Evolution
As demand for computational resources reaches unprecedented levels, research
is expanding into the use of complex material substrates for computing. In this
study, we interface with a model of a hydrodynamic system, under development by
a startup, as a computational reservoir and optimize its properties using an
evolution in materio approach. Input data are encoded as waves applied to our
shallow water reservoir, and the readout wave height is obtained at a fixed
detection point. We optimized the readout times and how inputs are mapped to
the wave amplitude or frequency using an evolutionary search algorithm, with
the objective of maximizing the system's ability to linearly separate
observations in the training data by maximizing the readout matrix determinant.
Applying evolutionary methods to this reservoir system substantially improved
separability on an XNOR task, in comparison to implementations with
hand-selected parameters. We also applied our approach to a regression task and
show that our approach improves out-of-sample accuracy. Results from this study
will inform how we interface with the physical reservoir in future work, and we
will use these methods to continue to optimize other aspects of the physical
implementation of this system as a computational reservoir.Comment: Accepted at the 2023 Genetic and Evolutionary Computation Conference
(GECCO 2023). 9 pages, 8 figure
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