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Quantum theory is notoriously counterintuitive, and yet remains entirely self-consistent when applied universally. Here we uncover a new manifestation of its unusual consequences. We demonstrate, theoretically and experimentally (by means of polarization-encoded single-photon qubits), that Heisenberg's uncertainty principle leads to the impossibility of stringing together logical deductions about outcomes of consecutive non-compatible measurements. This phenomenon resembles the geometry of a Penrose triangle, where each corner is locally consistent while the global structure is impossible. Besides this, we show how overlooking this non-trivial logical structure leads to the erroneous possibility of distinguishing non-orthogonal states with a single measurement.

Bell inequalities represent a milestone for contemporary Physics, both for quantum foundations investigation and technological applications (e.g., quantum communication and entanglement certification). Although loophole-free tests have been recently performed, a strong debate is still ongoing on the actual meaning of Bell inequality tests, for example on the possible additional hypotheses (end eventual loopholes) to be included in Bell's theorem, as well as on the implications for certain interpretations of quantum mechanics. A recent work [S. Virz\`i et al., Quantum Sci. Technol. 9, 045027 (2024)] challenges some of the statements appeared in this debate, achieving for the first time an experimental estimation of the entire Bell-CHSH parameter from a single entangled pair thanks to a weak-interaction-based measurement approach. Here we analyse the implications of this result for quantum mechanics foundations investigation, illustrating how it can tackle some of the aforementioned interpretations of Bell inequality tests and, more in general, of quantum mechanics itself.

We present an advanced quantum super-resolution imaging technique based on photon statistics measurement and its accurate modeling. Our reconstruction algorithm adapts to any kind of non-Poissonian emitters, outperforming the corresponding classical SOFI method. It offers sub-diffraction resolution improvement that scales with the $\sqrt{j}$, where $j$ is the highest order central moments of the photocounts. More remarkably, in combination with structured illumination a linear improvement with j can be reached. Through simulations and experiments, we prove our method's clear superiority over traditional SOFI, especially in low excitation light conditions, providing a promising avenue for non-invasive super-resolution microscopy of delicate samples.

In this paper we analyze and compare different movement sensors: micro-chip gesture-ID, leap motion, noitom mocap, and specially developed sensor for tapping and foot motion analysis. The main goal is to evaluate the accu-racy of measurements provided by the sensors. This study presents rele-vance, for instance, in tremor/Parkinson disease analysis as well as no touch mechanisms for activation and control of devices. This scenario is especially interesting in COVID-19 scenario. Removing the need to touch a surface, the risk of contagion is reduced., Comment: In: Esposito, A., Faundez-Zanuy, M., Morabito, F.C., Pasero, E. (eds) Applications of Artificial Intelligence and Neural Systems to Data Science. Smart Innovation, Systems and Technologies, vol 360. Springer, Singapore

In this paper, we present a new experimental apparatus for the measurement of the detection efficiency of free-space single-photon detectors based on the substitution method. For the first time, we extend the analysis to account for the wavelength dependence introduced by the transmissivity of the optical window in front of the detector's active area. Our method involves measuring the detector's response at different wavelengths and comparing it to a calibrated reference detector. This allows us to accurately quantify the efficiency variations due to the optical window's transmissivity. The results provide a comprehensive understanding of the wavelength-dependent efficiency, which is crucial for optimizing the performance of single-photon detectors in various applications, including quantum communication and photonics research. This characterization technique offers a significant advancement in the precision and reliability of single-photon detection efficiency measurements.

Unifying quantum theory and general relativity is the holy grail of contemporary physics. Nonetheless, the lack of experimental evidence driving this process led to a plethora of mathematical models with a substantial impossibility of discriminating among them or even establishing if gravity really needs to be quantized or if, vice versa, quantum mechanics must be "gravitized" at some scale. Recently, it has been proposed that the observation of the generation of entanglement by gravitational interaction, could represent a breakthrough demonstrating the quantum nature of gravity. A few experimental proposals have been advanced in this sense, but the extreme technological requirements (e.g., the need for free-falling gravitationally-interacting masses in a quantum superposition state) make their implementation still far ahead. Here we present a feasibility study for a table-top nanodiamond-based interferometer eventually enabling easier and less resource-demanding quantum gravity tests. With respect to the aforementioned proposals, by relying on quantum superpositions of steady massive (mesoscopic) objects our interferometer may allow exploiting just small-range electromagnetic fields (much easier to implement and control) and, at the same time, the re-utilization of the massive quantum probes exploited, inevitably lost in free-falling interferometric schemes., Comment: 8 pages, 5 figures

Word Ladders is a free mobile application for Android and iOS, developed for collecting linguistic data, specifically lists of words related to each other through semantic relations of categorical inclusion, within the Abstraction project (ERC-2021-STG-101039777). We hereby provide an overview of Word Ladders, explaining its game logic, motivation and expected results and applications to nlp tasks as well as to the investigation of cognitive scientific open questions

Camera calibration is a process of paramount importance in computer vision applications that require accurate quantitative measurements. The popular method developed by Zhang relies on the use of a large number of images of a planar grid of fiducial points captured in multiple poses. Although flexible and easy to implement, Zhang's method has some limitations. The simultaneous optimization of the entire parameter set, including the coefficients of a predefined distortion model, may result in poor distortion correction at the image boundaries or in miscalculation of the intrinsic parameters, even with a reasonably small reprojection error. Indeed, applications involving image stitching (e.g. multi-camera systems) require accurate mapping of distortion up to the outermost regions of the image. Moreover, intrinsic parameters affect the accuracy of camera pose estimation, which is fundamental for applications such as vision servoing in robot navigation and automated assembly. This paper proposes a method for estimating the complete set of calibration parameters from a single image of a planar speckle pattern covering the entire sensor. The correspondence between image points and physical points on the calibration target is obtained using Digital Image Correlation. The effective focal length and the extrinsic parameters are calculated separately after a prior evaluation of the principal point. At the end of the procedure, a dense and uniform model-free distortion map is obtained over the entire image. Synthetic data with different noise levels were used to test the feasibility of the proposed method and to compare its metrological performance with Zhang's method. Real-world tests demonstrate the potential of the developed method to reveal aspects of the image formation that are hidden by averaging over multiple images., Comment: Accepted manuscript

We study the Gibbs measure associated to the periodic cubic nonlinear Schr\"odinger equation. We establish a change of variable formula for this measure under the first step of the Birkhoff normal form reduction. We also consider the case of fractional dispersion.

Nitrogen-Vacancy (NV) centers in diamond are promising systems for quantum technologies, including quantum metrology and sensing. A promising strategy for the achievement of high sensitivity to external fields relies on the exploitation of large ensembles of NV centers, whose fabrication by ion implantation is upper limited by the amount of radiation damage introduced in the diamond lattice. In this works we demonstrate an approach to increase the density of NV centers upon the high-fluence implantation of MeV N2+ ions on a hot target substrate (>550 {\deg}C). Our results show that, with respect to room-temperature implantation, the high-temperature process increases the vacancy density threshold required for the irreversible conversion of diamond to a graphitic phase, thus enabling to achieve higher density ensembles. Furthermore, the formation efficiency of color centers was investigated on diamond substrates implanted at varying temperatures with MeV N2+ and Mg+ ions revealing that the formation efficiency of both NV centers and magnesium-vacancy (MgV) centers increases with the implantation temperature., Comment: 12 pages, 5 figures

Literate programming - the bringing together of program code and natural language narratives - has become a ubiquitous approach in the realm of data science. This methodology is appealing as well for the domain of Density Functional Theory (DFT) calculations, particularly for interactively developing new methodologies and workflows. However, effective use of literate programming is hampered by old programming paradigms and the difficulties associated with using High Performance Computing (HPC) resources. Here we present two Python libraries that aim to remove these hurdles. First, we describe the PyBigDFT library, which can be used to setup materials or molecular systems and provides high-level access to the wavelet based BigDFT code. We then present the related remotemanager library, which is able to serialize and execute arbitrary Python functions on remote supercomputers. We show how together these libraries enable transparent access to HPC based DFT calculations and can serve as building blocks for rapid prototyping and data exploration.

There has been extensive activity exploring the doping of semiconducting two-dimensional (2D) transition metal dichalcogenides in order to tune their electronic and magnetic properties. The outcome of doping depends on various factors, including the intrinsic properties of the host material, the nature of the dopants used, their spatial distribution as well as their interactions with other types of defects. A thorough atomic-level analysis is essential to fully understand these mechanisms. In this work, vanadium doped WSe$_2$ monolayer grown by molecular beam epitaxy is investigated using four-dimensional scanning transmission electron microscopy (4D-STEM). Through center of mass-based reconstruction, atomic scale maps are produced, allowing the visualization of both the electric field and the electrostatic potential around individual V atoms. To provide quantitative insights, these results are successfully compared with multislice image simulations based on ab initio calculations, accounting for lens aberrations. Finally, a negative charge around the V dopants is detected as a drop in the electrostatic potential, unambiguously demonstrating that 4D-STEM can be used to detect and to accurately analyze single dopant charge states in semiconducting 2D materials., Comment: 17 pages, 4 figures and Supporting Information

The impact of noise sources in real-world implementations of Twin-Field Quantum Key Distribution (TF-QKD) protocols is investigated, focusing on phase noise from photon sources and connecting fibers. This work emphasizes the role of laser quality, network topology, fiber length, arm balance, and detector performance in determining key rates. Remarkably, it reveals that the leading TF-QKD protocols are similarly affected by phase noise despite different mechanisms. This study demonstrates duty cycle improvements of over a factor of two through narrow-linewidth lasers and phase-control techniques, highlighting the potential synergy with high-precision time/frequency distribution services. Ultrastable lasers, evolving toward integration and miniaturization, offer promise for agile TF-QKD implementations on existing networks. Properly addressing phase noise and practical constraints allows for consistent key rate predictions, protocol selection, and layout design, crucial for establishing secure long-haul links for the Quantum Communication Infrastructures under development in several countries., Comment: 20 pages, 8 figures, 3 tables. Post-print version

Techniques based on classical and quantum correlations in light beams, such as ghost imaging, allow us to overcome many limitations of conventional imaging and sensing protocols. Despite their advantages, applications of such techniques are often limited in practical scenarios where the position and the longitudinal extension of the target object are unknown. In this work, we propose and experimentally demonstrate a novel imaging technique, named Light Field Ghost Imaging, that exploits light correlations and light field imaging principles to enable going beyond the limitations of ghost imaging in a wide range of applications. Notably, our technique removes the requirement to have prior knowledge of the object distance allowing the possibility of refocusing in post-processing, as well as performing 3D imaging while retaining all the benefits of ghost imaging protocols.

Compositionality is at the heart of computer science and several other areas of applied category theory such as computational linguistics, categorical quantum mechanics, interpretable AI, dynamical systems, compositional game theory, and Petri nets. However, the meaning of the term seems to vary across the many different applications. This work contributes to understanding, and in particular qualifying, different kinds of compositionality. Formally, we introduce invariants of categories that we call zeroth and first homotopy posets, generalising in a precise sense the pi0 and pi1 of a groupoid. These posets can be used to obtain a qualitative description of how far an object is from being terminal and a morphism is from being iso. In the context of applied category theory, this formal machinery gives us a way to qualitatively describe the "failures of compositionality", seen as failures of certain (op)lax functors to be strong, by classifying obstructions to the (op)laxators being isomorphisms. Failure of compositionality, for example for the interpretation of a categorical syntax in a semantic universe, can both be a bad thing and a good thing, which we illustrate by respective examples in graph theory and quantum theory., Comment: In Proceedings ACT 2023, arXiv:2312.08138

Epitaxial growth has become a promising route to achieve highly crystalline continuous two-dimensional layers. However, high-quality layer production with expected electrical properties is still challenging due to the defects induced by the coalescence between imperfectly aligned domains. In order to control their intrinsic properties at the device scale, the synthesized materials should be described as a patchwork of coalesced domains. Here, we report multi-scale and multistructural analysis on highly oriented epitaxial WS$_2$ and WSe$_2$ monolayers using scanning transmission electron microscopy (STEM) techniques. Characteristic domain junctions are first identified and classified based on the detailed atomic structure analysis using aberration corrected STEM imaging. Mapping orientation, polar direction and phase at the micrometer scale using four-dimensional STEM enabled to access the density and the distribution of the specific domain junctions. Our results validate a readily applicable process for the study of highly oriented epitaxial transition metal dichalcogenides, providing an overview of synthesized materials from large scale down to atomic scale with multiple structural information., Comment: 22 pages, 6 figures and Supplementary Information

In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. We discuss here general recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z=1 to 96 and characterizing 10 prototypical cubic compounds for each element: 4 unaries and 6 oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies)., Comment: Main text: 23 pages, 4 figures. Supplementary: 68 pages. Nature Review Physics 2023

Brain cell structure and function reflect neurodevelopment, plasticity and ageing, and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to non-invasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MR spectroscopy (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modelling and interpretation of results. These challenges were the motivation behind the organisation of the Lorentz Workshop on 'Best Practices and Tools for Diffusion MR Spectroscopy' held in Leiden in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting and modelling dMRS data and provides links to useful resources., Comment: 49 pages, 5 figures, 5 tables, supplementary material