2,901 research outputs found
DSMC investigation of rarefied gas flow through diverging micro- and nanochannels
Direct simulation Monte Carlo (DSMC) method with simplified Bernoulli-trials
(SBT) collision scheme has been used to study the rarefied pressure-driven
nitrogen flow through diverging microchannels. The fluid behaviours flowing
between two plates with different divergence angles ranging between 0
to 17 are described at different pressure ratios
(1.52.5) and Knudsen numbers (0.03Kn12.7). The
primary flow field properties, including pressure, velocity, and temperature,
are presented for divergent microchannels and are compared with those of a
microchannel with a uniform cross-section. The variations of the flow field
properties in divergent microchannels, which are influenced by the area change,
the channel pressure ratio and the rarefication are discussed. The results show
no flow separation in divergent microchannels for all the range of simulation
parameters studied in the present work. It has been found that a divergent
channel can carry higher amounts of mass in comparison with an equivalent
straight channel geometry. A correlation between the mass flow rate through
microchannels, the divergence angle, the pressure ratio, and the Knudsen number
has been suggested. The present numerical findings prove the occurrence of
Knudsen minimum phenomenon in micro- and Nano- channels with non-uniform
cross-sections.Comment: Accepted manuscript; 25 Pages and 11 Figures; "Microfluidics and
Nanofluidics
Quantum Tetrahedra
We discuss in details the role of Wigner 6j symbol as the basic building
block unifying such different fields as state sum models for quantum geometry,
topological quantum field theory, statistical lattice models and quantum
computing. The apparent twofold nature of the 6j symbol displayed in quantum
field theory and quantum computing -a quantum tetrahedron and a computational
gate- is shown to merge together in a unified quantum-computational SU(2)-state
sum framework
Low-cost error mitigation by symmetry verification
We investigate the performance of error mitigation via measurement of
conserved symmetries on near-term devices. We present two protocols to measure
conserved symmetries during the bulk of an experiment, and develop a zero-cost
post-processing protocol which is equivalent to a variant of the quantum
subspace expansion. We develop methods for inserting global and local symetries
into quantum algorithms, and for adjusting natural symmetries of the problem to
boost their mitigation against different error channels. We demonstrate these
techniques on two- and four-qubit simulations of the hydrogen molecule (using a
classical density-matrix simulator), finding up to an order of magnitude
reduction of the error in obtaining the ground state dissociation curve.Comment: Published versio
Simulating generic spin-boson models with matrix product states
The global coupling of few-level quantum systems ("spins") to a discrete set
of bosonic modes is a key ingredient for many applications in quantum science,
including large-scale entanglement generation, quantum simulation of the
dynamics of long-range interacting spin models, and hybrid platforms for force
and spin sensing. We present a general numerical framework for treating the
out-of-equilibrium dynamics of such models based on matrix product states. Our
approach applies for generic spin-boson systems: it treats any spatial and
operator dependence of the two-body spin-boson coupling and places no
restrictions on relative energy scales. We show that the full counting
statistics of collective spin measurements and infidelity of quantum simulation
due to spin-boson entanglement, both of which are difficult to obtain by other
techniques, are readily calculable in our approach. We benchmark our method
using a recently developed exact solution for a particular spin-boson coupling
relevant to trapped ion quantum simulators. Finally, we show how decoherence
can be incorporated within our framework using the method of quantum
trajectories, and study the dynamics of an open-system spin-boson model with
spatially non-uniform spin-boson coupling relevant for trapped atomic ion
crystals in the presence of molecular ion impurities.Comment: 13 pages+refs. 13 figure
Backpropagation training in adaptive quantum networks
We introduce a robust, error-tolerant adaptive training algorithm for
generalized learning paradigms in high-dimensional superposed quantum networks,
or \emph{adaptive quantum networks}. The formalized procedure applies standard
backpropagation training across a coherent ensemble of discrete topological
configurations of individual neural networks, each of which is formally merged
into appropriate linear superposition within a predefined, decoherence-free
subspace. Quantum parallelism facilitates simultaneous training and revision of
the system within this coherent state space, resulting in accelerated
convergence to a stable network attractor under consequent iteration of the
implemented backpropagation algorithm. Parallel evolution of linear superposed
networks incorporating backpropagation training provides quantitative,
numerical indications for optimization of both single-neuron activation
functions and optimal reconfiguration of whole-network quantum structure.Comment: Talk presented at "Quantum Structures - 2008", Gdansk, Polan
BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations
Objective: The advent of High-Performance Computing (HPC) in recent years has
led to its increasing use in brain study through computational models. The
scale and complexity of such models are constantly increasing, leading to
challenging computational requirements. Even though modern HPC platforms can
often deal with such challenges, the vast diversity of the modeling field does
not permit for a single acceleration (or homogeneous) platform to effectively
address the complete array of modeling requirements. Approach: In this paper we
propose and build BrainFrame, a heterogeneous acceleration platform,
incorporating three distinct acceleration technologies, a Dataflow Engine, a
Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform.
As a challenging proof of concept, we analyze the performance of BrainFrame on
different instances of a state-of-the-art neuron model, modeling the Inferior-
Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley
representation. The model instances take into account not only the neuronal-
network dimensions but also different network-connectivity circumstances that
can drastically change application workload characteristics. Main results: The
synthetic approach of three HPC technologies demonstrated that BrainFrame is
better able to cope with the modeling diversity encountered. Our performance
analysis shows clearly that the model directly affect performance and all three
technologies are required to cope with all the model use cases.Comment: 16 pages, 18 figures, 5 table
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