42,452 research outputs found
Complexity, parallel computation and statistical physics
The intuition that a long history is required for the emergence of complexity
in natural systems is formalized using the notion of depth. The depth of a
system is defined in terms of the number of parallel computational steps needed
to simulate it. Depth provides an objective, irreducible measure of history
applicable to systems of the kind studied in statistical physics. It is argued
that physical complexity cannot occur in the absence of substantial depth and
that depth is a useful proxy for physical complexity. The ideas are illustrated
for a variety of systems in statistical physics.Comment: 21 pages, 7 figure
Guided Machine Learning for power grid segmentation
The segmentation of large scale power grids into zones is crucial for control
room operators when managing the grid complexity near real time. In this paper
we propose a new method in two steps which is able to automatically do this
segmentation, while taking into account the real time context, in order to help
them handle shifting dynamics. Our method relies on a "guided" machine learning
approach. As a first step, we define and compute a task specific "Influence
Graph" in a guided manner. We indeed simulate on a grid state chosen
interventions, representative of our task of interest (managing active power
flows in our case). For visualization and interpretation, we then build a
higher representation of the grid relevant to this task by applying the graph
community detection algorithm \textit{Infomap} on this Influence Graph. To
illustrate our method and demonstrate its practical interest, we apply it on
commonly used systems, the IEEE-14 and IEEE-118. We show promising and original
interpretable results, especially on the previously well studied RTS-96 system
for grid segmentation. We eventually share initial investigation and results on
a large-scale system, the French power grid, whose segmentation had a
surprising resemblance with RTE's historical partitioning
Quantum computing and the entanglement frontier - Rapporteur talk at the 25th Solvay Conference
Quantum information science explores the frontier of highly complex quantum states,
the "entanglement frontier". This study is motivated by the observation (widely believed
but unproven) that classical systems cannot simulate highly entangled quantum systems
efficiently, and we hope to hasten the day when well controlled quantum systems can
perform tasks surpassing what can be done in the classical world. One way to achieve
such "quantum supremacy" would be to run an algorithm on a quantum computer which
solves a problem with a super-polynomial speedup relative to classical computers, but
there may be other ways that can be achieved sooner, such as simulating exotic quantum
states of strongly correlated matter. To operate a large scale quantum computer reliably
we will need to overcome the debilitating effects of decoherence, which might be done
using "standard" quantum hardware protected by quantum error-correcting codes, or by
exploiting the nonabelian quantum statistics of anyons realized in solid state systems,
or by combining both methods. Only by challenging the entanglement frontier will we
learn whether Nature provides extravagant resources far beyond what the classical world
would allow
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
Using Quantum Computers for Quantum Simulation
Numerical simulation of quantum systems is crucial to further our
understanding of natural phenomena. Many systems of key interest and
importance, in areas such as superconducting materials and quantum chemistry,
are thought to be described by models which we cannot solve with sufficient
accuracy, neither analytically nor numerically with classical computers. Using
a quantum computer to simulate such quantum systems has been viewed as a key
application of quantum computation from the very beginning of the field in the
1980s. Moreover, useful results beyond the reach of classical computation are
expected to be accessible with fewer than a hundred qubits, making quantum
simulation potentially one of the earliest practical applications of quantum
computers. In this paper we survey the theoretical and experimental development
of quantum simulation using quantum computers, from the first ideas to the
intense research efforts currently underway.Comment: 43 pages, 136 references, review article, v2 major revisions in
response to referee comments, v3 significant revisions, identical to
published version apart from format, ArXiv version has table of contents and
references in alphabetical orde
The use of real time digital simulation and hardware in the loop to de-risk novel control algorithms
Low power demonstrators are commonly used to validate novel control algorithms. However, the response of the demonstrator to network transients and faults is often unexplored. The importance of this work has, in the past, justified facilities such as the T45 Shore Integration Test Facility (SITF) at the Electric Ship Technology Demonstrator (ESTD). This paper presents the use of real time digital simulation and hardware in the loop to de-risk a innovative control algorithm with respect to network transients and faults. A novel feature of the study is the modelling of events at the power electronics level (time steps of circa 2 ÎĽs) and the system level (time steps of circa 50 ÎĽs)
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