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

Ray splitting in paraxial optical cavities

We present a numerical investigation of the ray dynamics in a paraxial optical cavity when a ray splitting mechanism is present. The cavity is a conventional two-mirror stable resonator and the ray splitting is achieved by inserting an optical beam splitter perpendicular to the cavity axis. We show that depending on the position of the beam splitter the optical resonator can become unstable and the ray dynamics displays a positive Lyapunov exponent.Comment: 13 pages, 7 figures, 1 tabl