Quantum adiabatic machine learning by zooming into a region of the energy surface

Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose QAML-Z, an algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the receiver operating characteristic curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks

Defining and detecting quantum speedup

The development of small-scale digital and analog quantum devices raises the question of how to fairly assess and compare the computational power of classical and quantum devices, and of how to detect quantum speedup. Here we show how to define and measure quantum speedup in various scenarios, and how to avoid pitfalls that might mask or fake quantum speedup. We illustrate our discussion with data from a randomized benchmark test on a D-Wave Two device with up to 503 qubits. Comparing the performance of the device on random spin glass instances with limited precision to simulated classical and quantum annealers, we find no evidence of quantum speedup when the entire data set is considered, and obtain inconclusive results when comparing subsets of instances on an instance-by-instance basis. Our results for one particular benchmark do not rule out the possibility of speedup for other classes of problems and illustrate that quantum speedup is elusive and can depend on the question posed

Ecological determinants of fungal diversity on dead wood in European forests

International audienceThe fine-scale ecological determinants for wood-inhabiting aphyllophoroid basidiomycetes were investigated with statistical analyses of the occurrence of fruit bodies on woody debris collected in Switzerland and Ukraine. Three substrate descriptors were considered: diameter, degree of decomposition and host tree species. By means of Multiple Regression Trees, thresholds in the response of fungal communities to these local environmental descriptors were detected. Three classes for diameter, as well as for degree of decomposition were thus delimited. They revealed the importance of very small sizes, which were not reported in the literature so far: the relevant diameter class limits were about 0.72 cm and 1.35 cm. Within the host tree species, a clear distinction between coniferous and broadleaf species was found. The next splits followed rather climatic determinants of tree distribution than taxonomical entities such as families or genera. The fidelity of the 59 fungal species to diameter classes, decomposition classes and host tree species was measured by the Dufrêne-Legendre index and only significant responses after permutation tests were retained. This brought new insights on the ecology of many wood-inhabiting aphyllophoroid basidiomycetes. Redundancy Analysis was applied to investigate the response of fungal species to diameter and degree of decomposition of woody debris from the most common host tree species, Fagus sylvatica. This direct gradient analysis made it possible to reconstruct the succession of fungal species along the wood decomposition process

Isolation of oxalotrophic bacteria able to disperse on fungal mycelium

A technique based on an inverted Petri dish system was developed for the growth and isolation of soil oxalotrophic bacteria able to disperse on fungal mycelia. The method is related to the ‘fungal highways' dispersion theory in which mycelial fungal networks allow active movement of bacteria in soil. Quantification of this phenomenon showed that bacterial dispersal occurs preferentially in upper soil horizons. Eight bacteria and one fungal strain were isolated by this method. The oxalotrophic activity of the isolated bacteria was confirmed through calcium oxalate dissolution in solid selective medium. After separation of the bacteria-fungus couple, partial sequencing of the 16S and the ITS1 and ITS2 sequences of the ribosomal RNA genes were used for the identification of bacteria and the associated fungus. The isolated oxalotrophic bacteria included strains related to Stenotrophomonas, Achromobacter, Lysobacter, Pseudomonas, Agrobacterium, Cohnella, and Variovorax. The recovered fungus corresponded to Trichoderma sp. A test carried out to verify bacterial transport in an unsaturated medium showed that all the isolated bacteria were able to migrate on Trichoderma hyphae or glass fibers to re-colonize an oxalate-rich medium. The results highlight the importance of fungus-driven bacterial dispersal to understand the functional role of oxalotrophic bacteria and fungi in soil

QUALITY OF PROCESS ? A BUSINESS PROCESS PERSPECTIVE ON QUALITY OF SERVICE

The fundamental paradigm shift from a product- to a service-oriented economy implies novel technical and organizational challenges. The resulting dynamic of the technical infrastructure and the increasing development towards requesting external business services to be integrated into end-to-end business processes requires mechanisms ensuring the reliability of the organization?s composed services, workflows and business processes. From a business perspective, QoS characteristics defined based on technical services within the infrastructural layer have to be aggregated to more business-relevant Key Performance Indicators on business process layer to express the Quality of Process. These KPIs represent quality that is highly related to the business?s performance (e.g. processing time of a business service) and are crucial for achieving predefined goals in order to stay competitive in the market. The contribution of this paper is threefold: We (i) provide an in-depth requirements analysis for such a holistic quality management framework, we (ii) develop a holistic aggregation framework which enables service level aggregation incorporating the loosely coupled structure of business processes with invoked systems and services in an instance based manner. To demonstrate the expressive power of our framework we (iii) provide an exemplary industrial application scenario and illustrate the functioning and interplay of the designed artifacts