2,550 research outputs found
Ordered Measurements of Permutationally-Symmetric Qubit Strings
We show that any sequence of measurements on a permutationally-symmetric
(pure or mixed) multi-qubit string leaves the unmeasured qubit substring also
permutationally-symmetric. In addition, we show that the measurement
probabilities for an arbitrary sequence of single-qubit measurements are
independent of how many unmeasured qubits have been lost prior to the
measurement. Our results are valuable for quantum information processing of
indistinguishable particles by post-selection, e.g. in cases where the results
of an experiment are discarded conditioned upon the occurrence of a given event
such as particle loss. Furthermore, our results are important for the design of
adaptive-measurement strategies, e.g. a series of measurements where for each
measurement instance, the measurement basis is chosen depending on prior
measurement results.Comment: 13 page
Effects of boundary roughness on a Q-factor of whispering-gallery-mode lasing microdisk cavities
We perform numerical studies of the effect of sidewall imperfections on the
resonant state broadening of the optical microdisk cavities for lasing
applications. We demonstrate that even small edge roughness causes a drastic
degradation of high-Q whispering gallery (WG) mode resonances reducing their
Q-values by many orders of magnitude. At the same time, low-Q WG resonances are
rather insensitive to the surface roughness. The results of numerical
simulation obtained using the scattering matrix technique, are analyzed and
explained in terms of wave reflection at a curved dielectric interface combined
with the examination of Poincare surface of sections in the classical ray
picture.Comment: 4 pages, 3 figure
An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes
Quantum-enhanced metrology infers an unknown quantity with accuracy beyond
the standard quantum limit (SQL). Feedback-based metrological techniques are
promising for beating the SQL but devising the feedback procedures is difficult
and inefficient. Here we introduce an efficient self-learning
swarm-intelligence algorithm for devising feedback-based quantum metrological
procedures. Our algorithm can be trained with simulated or real-world trials
and accommodates experimental imperfections, losses, and decoherence
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
The Particle Swarm Optimization Policy (PSO-P) has been recently introduced
and proven to produce remarkable results on interacting with academic
reinforcement learning benchmarks in an off-policy, batch-based setting. To
further investigate the properties and feasibility on real-world applications,
this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a
novel reinforcement learning (RL) benchmark that aims at being realistic by
including a variety of aspects found in industrial applications, like
continuous state and action spaces, a high dimensional, partially observable
state space, delayed effects, and complex stochasticity. The experimental
results of PSO-P on IB are compared to results of closed-form control policies
derived from the model-based Recurrent Control Neural Network (RCNN) and the
model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not
only of interest for academic benchmarks, but also for real-world industrial
applications, since it also yielded the best performing policy in our IB
setting. Compared to other well established RL techniques, PSO-P produced
outstanding results in performance and robustness, requiring only a relatively
low amount of effort in finding adequate parameters or making complex design
decisions
A Benchmark Environment Motivated by Industrial Control Problems
In the research area of reinforcement learning (RL), frequently novel and
promising methods are developed and introduced to the RL community. However,
although many researchers are keen to apply their methods on real-world
problems, implementing such methods in real industry environments often is a
frustrating and tedious process. Generally, academic research groups have only
limited access to real industrial data and applications. For this reason, new
methods are usually developed, evaluated and compared by using artificial
software benchmarks. On one hand, these benchmarks are designed to provide
interpretable RL training scenarios and detailed insight into the learning
process of the method on hand. On the other hand, they usually do not share
much similarity with industrial real-world applications. For this reason we
used our industry experience to design a benchmark which bridges the gap
between freely available, documented, and motivated artificial benchmarks and
properties of real industrial problems. The resulting industrial benchmark (IB)
has been made publicly available to the RL community by publishing its Java and
Python code, including an OpenAI Gym wrapper, on Github. In this paper we
motivate and describe in detail the IB's dynamics and identify prototypic
experimental settings that capture common situations in real-world industry
control problems
A Benchmark Environment Motivated by Industrial Control Problems
In the research area of reinforcement learning (RL), frequently novel and
promising methods are developed and introduced to the RL community. However,
although many researchers are keen to apply their methods on real-world
problems, implementing such methods in real industry environments often is a
frustrating and tedious process. Generally, academic research groups have only
limited access to real industrial data and applications. For this reason, new
methods are usually developed, evaluated and compared by using artificial
software benchmarks. On one hand, these benchmarks are designed to provide
interpretable RL training scenarios and detailed insight into the learning
process of the method on hand. On the other hand, they usually do not share
much similarity with industrial real-world applications. For this reason we
used our industry experience to design a benchmark which bridges the gap
between freely available, documented, and motivated artificial benchmarks and
properties of real industrial problems. The resulting industrial benchmark (IB)
has been made publicly available to the RL community by publishing its Java and
Python code, including an OpenAI Gym wrapper, on Github. In this paper we
motivate and describe in detail the IB's dynamics and identify prototypic
experimental settings that capture common situations in real-world industry
control problems
Residual disorder and diffusion in thin Heusler alloy films
Co2FeSi/GaAs(110) and Co2FeSi/GaAs(111)B hybrid structures were grown by
molecular-beam epitaxy and characterized by transmission electron microscopy
(TEM) and X-ray diffraction. The films contained inhomogeneous distributions of
ordered L2_1 and B2 phases. The average stoichiometry was controlled by lattice
parameter measurements, however diffusion processes lead to inhomogeneities of
the atomic concentrations and the degradation of the interface, influencing
long-range order. An average long-range order of 30-60% was measured by
grazing-incidence X-ray diffraction, i.e. the as-grown Co2FeSi films were
highly but not fully ordered. Lateral inhomogeneities of the spatial
distribution of long-range order in Co2FeSi were found using dark-field TEM
images taken with superlattice reflections
Rubritalea marina gen. nov., sp. nov., a marine representative of the phylum 'Verrucomicrobia', isolated from a sponge (Porifera)
A marine bacterium, strain Pol012T, was isolated from the Mediterranean sponge Axinella polypoides and subsequently characterized as belonging to subphylum 1 of the phylum ‘Verrucomicrobia’. Strain Pol012T was non-motile, Gram-negative, coccoid or rod-shaped and red in colour. The menaquinones MK-8 and MK-9 were detected. The G+C content of the genomic DNA was 50.9 mol%. Growth was possible at temperatures between 8 and 30 °C and at pH values between 6.8 and 8.2. The closest cultured relative of strain Pol012T was Akkermansia muciniphila (83 % sequence similarity), while the closest environmental 16S rRNA gene sequence was the marine clone Arctic96BD-2 (95 % sequence similarity). Strain Pol012T is the first marine pure-culture representative of ‘Verrucomicrobia’ subphylum 1 and represents a novel genus and species, for which the name Rubritalea marina gen. nov., sp. nov. is proposed. The type strain is Pol012T (=DSM 177716T=CIP 108984T).
The GenBank/EMBL/DDBJ accession number for the 16S rRNA gene sequence of strain Pol012T is DQ302104, and those for verrucomicrobial 16S rRNA gene sequences from sponges and seawater are DQ302105–DQ302120
Surface morphology of AlGaN/GaN heterostructures grown on bulk GaN by MBE
In this report the influence of the growth conditions on the surface morphology of AlGaN/GaN heterostructures grown on sapphire-based and bulk GaN substrates is nondestructively investigated with focus on the decoration of defects and the surface roughness. Under Ga-rich conditions specific types of dislocations are unintentionally decorated with shallow hillocks. In contrast, under Ga-lean conditions deep pits are inherently formed at these defect sites. The structural data show that the dislocation density of the substrate sets the limit for the density of dislocation-mediated surface structures after MBE overgrowth and no noticeable amount of surface defects is introduced during the MBE procedure. Moreover, the transfer of crystallographic information, e.g. the miscut of the substrate to the overgrown structure, is confirmed. The combination of our MBE overgrowth with the employed surface morphology analysis by atomic force microscopy (AFM) provides a unique possibility for a nondestructive, retrospective analysis of the original substrate defect density prior to device processing
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