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We present an optimization-driven approach to creating a double-tough ceramic material with a brick-and-mortar microstructure, where the mortar is itself transformation-toughened, engineered with the goal of simultaneously achieving high strength and fracture toughness levels. Specifically, we design a material where high-strength alumina bricks are interconnected via a ceria-stabilized zirconia mortar. As the design of such a material, driven by multiscale toughening mechanisms, requires a laborious trial-and-error approach, we propose a Bayesian optimization framework as an integral part of our methodology to streamline and accelerate the design process. We use a Gaussian process to emulate the material's mechanical response and implement a cost-aware batch Bayesian optimization to efficiently identify optimal design process parameters, accounting for the cost of experimentally varying them. This approach expedites the optimization of the material's mechanical properties. As a result, we develop a bio-inspired all-ceramic composite that exhibits an exceptional balance between bending strength (704 MPa), and fracture toughness (13.6 MPa m^0.5).

In this work, we explore how resources are distributed among the states generated by quantum random circuits (QRC). We focus on multipartite non-locality, but we also analyze quantum correlations by appealing to different entanglement and non-classicality measures. We compare universal vs non-universal sets of gates to gain insight into the problem of explaining quantum advantage. By comparing the results obtained with ideal (noiseless) vs noisy intermediate-scale quantum (NISQ) devices, we lay the basis of a certification protocol, which aims to quantify how robust is the resources distribution among the states that a given device can generate., Comment: 20 pages, 18 figures

Supercrystalline nanocomposites (SCNCs) are inorganic-organic hybrid materials with a unique periodic nanostructure, and as such they have been gaining growing attention for their intriguing functional properties and parallelisms with hierarchical biomaterials. Their mechanical behavior remains, however, poorly understood, even though its understanding and control are of paramount importance to allow SCNCs implementation into devices. An important aspect that has not been tackled yet is their time-dependent deformation behavior, which is nevertheless expected to play an important role in materials containing such a distribution of organic phase. Hereby, we report on the creep of ceramic-organic SCNCs with varying degrees of organic crosslinking, as assessed via nanoindentation. Creep strains and their partial recoverability are observed, hinting at the co-presence of viscoelasticity and viscoplasticity, and a clear effect of crosslinking in decreasing the overall material deformability. We rationalize our experimental observations with the analysis of stress exponent and activation volume, resulting in a power-law breakdown behavior and governing deformation mechanisms occurring at the organic sub-nm interfaces scale, in terms of organic ligands rearrangement. The set of results is reinforced by the evaluation of the strain rate sensitivity via strain rate jump tests, and the assessment of the effect of oscillations during continuous stiffness measurement mode.

How are abstract concepts and musical themes recognized on the basis of some previous experience? It is interesting to compare the different behaviors of human and of artificial intelligences with respect to this problem. Generally, a human mind that abstracts a concept (say, table) from a given set of known examples creates a table-Gestalt: a kind of vague and out of focus image that does not fully correspond to a particular table with well determined features. A similar situation arises in the case of musical themes. Can the construction of a gestaltic pattern, which is so natural for human minds, be taught to an intelligent machine? This problem can be successfully discussed in the framework of a quantum approach to pattern recognition and to machine learning. The basic idea is replacing classical data sets with quantum data sets, where either objects or musical themes can be formally represented as pieces of quantum information, involving the uncertainties and the ambiguities that characterize the quantum world. In this framework, the intuitive concept of Gestalt can be simulated by the mathematical concept of positive centroid of a given quantum data set. Accordingly, the crucial problem "how can we classify a new object or a new musical theme (we have listened to) on the basis of a previous experience?" can be dealt with in terms of some special quantum similarity-relations. Although recognition procedures are different for human and for artificial intelligences, there is a common method of "facing the problems" that seems to work in both cases.

In this paper we investigate the connection between quantum information theory and machine learning. In particular, we show how quantum state discrimination can represent a useful tool to address the standard classification problem in machine learning. Previous studies have shown that the optimal quantum measurement theory developed in the context of quantum information theory and quantum communication can inspire a new binary classification algorithm that can achieve higher inference accuracy for various datasets. Here we propose a model for arbitrary multiclass classification inspired by quantum state discrimination, which is enabled by encoding the data in the space of linear operators on a Hilbert space. While our algorithm is quantum-inspired, it can be implemented on classical hardware, thereby permitting immediate applications., Comment: 21 pages, 3 figures

The controlled creation of quantum emitters in diamond represents a major research effort in the fabrication of single-photon devices. Here, we present the scalable production of silicon-vacancy (SiV) color centers in single-crystal diamond by ion implantation. The lateral position of the SiV is spatially controlled by a 1-$\mu$m pinhole placed in front of the sample, which can be moved nanometer precise using a piezo stage. The initial implantation position is controlled by monitoring the ion beam position with a camera. Hereby, silicon ions are implanted at the desired spots in an area comparable to the diffraction limit. We discuss the role of ions scattered by the pinhole and the activation yield of the SiV color centers for the creation of single quantum emitters., Comment: 11 pages, 4 figures

Phosphorus-doped diamond is relevant for applications in sensing, optoelectronics and quantum photonics, since the unique optical properties of color centers in diamond can be combined with the n-type conductivity attained by the inclusion of phosphorus. Here, we investigate the photoluminescence signal of the nitrogen-vacancy and silicon-vacancy color centers in phosphorus-doped diamond as a function of temperature starting from ambient conditions up to about 100$^\circ$ Celsius, focusing on the zero-phonon line (ZPL). We find that the wavelength and width of the ZPL of the two color centers exhibit a comparable dependence on temperature, despite the strong difference in the photoluminescence spectra. Moreover, the temperature sensitivity of the ZPL of the silicon-vacancy center is not significantly affected by phosphorus-doping, as we infer by comparison with silicon-vacancy centers in electronic-grade single-crystal diamond., Comment: 5 pages, 1 figure

We investigate the optical properties of polycrystalline diamond membranes containing silicon-vacancy (SiV) color centers in combination with other nano-analytical techniques. We analyze the correlation between the Raman signal, the SiV emission, and the background luminescence in the crystalline grains and in the grain boundaries, identifying conditions for the addressability of single SiV centers. Moreover, we perform a scanning transmission electron microscopy (STEM) analysis, which associates the microscopic structure of the membranes and the evolution of the diamond crystal along the growth direction with the photoluminescence properties, as well as a time-of-flight secondary ion mass spectrometry (ToF-SIMS) to address the distribution of silicon in implanted and un-implanted membranes. The results of the STEM and ToF-SIMS studies are consistent with the outcome of the optical measurements and provide useful insight into the preparation of polycrystalline samples for quantum nano-optics., Comment: 21 pages, 8 figures

People with Parkinson's disease must be regularly monitored by their physician to observe how the disease is progressing and potentially adjust treatment plans to mitigate the symptoms. Monitoring the progression of the disease through a voice recording captured by the patient at their own home can make the process faster and less stressful. Using a dataset of voice recordings of 42 people with early-stage Parkinson's disease over a time span of 6 months, we applied multiple machine learning techniques to find a correlation between the voice recording and the patient's motor UPDRS score. We approached this problem using a multitude of both regression and classification techniques. Much of this paper is dedicated to mapping the voice data to motor UPDRS scores using regression techniques in order to obtain a more precise value for unknown instances. Through this comparative study of variant machine learning methods, we realized some old machine learning methods like trees outperform cutting edge deep learning models on numerous tabular datasets., Comment: Accepted at "HIMS'20 - The 6th Int'l Conf on Health Informatics and Medical Systems"; https://americancse.org/events/csce2020/conferences/hims20

The controlled creation of color centers in phosphorus-doped (n-type) diamond can facilitate the electronics integration of quantum photonics devices, such as single-photon sources operating upon electrical injection. Silicon vacancy (SiV) color centers are promising candidates, but so far the conditions for single-photon emission in phosphorus-doped diamond have not been investigated. In this study, we create SiV color centers in diamond samples with different phosphorus concentrations and show that the fluorescence background due to doping, nitrogen-impurities and ion implantation induced defects can be significantly suppressed. Single-photon emitters in phosphorus-doped diamond are obtained at the low Si-ion implantation fluences.

We continue our investigation of paraorthomodular BZ*-lattices (PBZ*-lattices), started in \cite{GLP1+,PBZ2,rgcmfp,pbzsums,pbz5}. We shed further light on the structure of the subvariety lattice of the variety $\mathbb{PBZL}^{\ast }$ of PBZ*-lattices; in particular, we provide axiomatic bases for some of its members. Further, we show that some distributive subvarieties of $\mathbb{PBZL}^{\ast }$ are term-equivalent to well-known varieties of expanded Kleene lattices or of nonclassical modal algebras. By so doing, we somehow help the reader to locate PBZ*-lattices on the atlas of algebraic structures for nonclassical logics., Comment: 24 pages

Color centers in diamond represent a promising platform for developing solid-state single-photon sources and spin-photon interfaces as building blocks for photonics-based quantum technologies. However, although they exhibit a combination of features that make them so attractive, they also suffer from limited control in their emission properties, such as brightness, directionality, and polarization to cite a few. In this paper we present implementations and the experimental investigation of planar Yagi-Uda antennas in diamond, demonstrating highly efficient light extraction and directional emission from silicon-vacancy color centers created in thin diamond membranes.

A holistic extension of classical propositional logic is introduced in the framework of quantum computation with mixed states. The concepts of tautology and contradiction are investigated in this extensions. A special family of quantum states are investigated as particular cases of "holistic" contradiction.