Learning Fermionic Correlations by Evolving with Random Translationally Invariant Hamiltonians.

Schemes of classical shadows have been developed to facilitate the readout of digital quantum devices, but similar tools for analog quantum simulators are scarce and experimentally impractical. In this Letter, we provide a measurement scheme for fermionic quantum devices that estimates second and fourth order correlation functions by means of free fermionic, translationally invariant evolutions-or quenches-and measurements in the mode occupation number basis. We precisely characterize what correlation functions can be recovered and equip the estimates with rigorous bounds on sample complexities, a particularly important feature in light of the difficulty of getting good statistics in reasonable experimental platforms, with measurements being slow.

Verifiable measurement-based quantum random sampling with trapped ions.

Quantum computers are now on the brink of outperforming their classical counterparts. One way to demonstrate the advantage of quantum computation is through quantum random sampling performed on quantum computing devices. However, existing tools for verifying that a quantum device indeed performed the classically intractable sampling task are either impractical or not scalable to the quantum advantage regime.

Abraded optical fibre-based dynamic range force sensor for tissue palpation.

Tactile information acquired through palpation plays a crucial role in relation to surface characterisation and tissue differentiation - an essential clinical requirement during surgery. In the case of Minimally Invasive Surgery, access is restricted, and tactile feedback available to surgeons is therefore reduced. This paper presents a novel stiffness controllable, dynamic force range sensor that can provide remote haptic feedback.

Differential rates of Mycobacterium tuberculosis transmission associate with host-pathogen sympatry.

Several human-adapted Mycobacterium tuberculosis complex (Mtbc) lineages exhibit a restricted geographical distribution globally. These lineages are hypothesized to transmit more effectively among sympatric hosts, that is, those that share the same geographical area, though this is yet to be confirmed while controlling for exposure, social networks and disease risk after exposure. Using pathogen genomic and contact tracing data from 2,279 tuberculosis cases linked to 12,749 contacts from three low-incidence cities, we show that geographically restricted Mtbc lineages were less transmissible than lineages that have a widespread global distribution.

Shallow Shadows: Expectation Estimation Using Low-Depth Random Clifford Circuits.

We provide practical and powerful schemes for learning properties of a quantum state using a small number of measurements. Specifically, we present a randomized measurement scheme modulated by the depth of a random quantum circuit in one spatial dimension. This scheme interpolates between two known classical shadows schemes based on random Pauli measurements and random Clifford measurements.

SignEEG v1.0: Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems.

Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality's strengths.

Pseudomagic Quantum States.

Notions of nonstabilizerness, or "magic," quantify how nonclassical quantum states are in a precise sense: states exhibiting low nonstabilizerness preclude quantum advantage. We introduce "pseudomagic" ensembles of quantum states that, despite low nonstabilizerness, are computationally indistinguishable from those with high nonstabilizerness. Previously, such computational indistinguishability has been studied with respect to entanglement, introducing the concept of pseudoentanglement.

Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery.

Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models is a common concern. To address this issue, explainable AI (XAI) methods can be employed.

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification.

Introduction: Artificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with 'untrained' or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign.

Discovering Subgroups of Children With High Mortality in Urban Guinea-Bissau: Exploratory and Validation Cohort Study.

Background: The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions.

Objective: This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa.

Secure federated learning for Alzheimer's disease detection.

Machine Learning (ML) is considered a promising tool to aid and accelerate diagnosis in various medical areas, including neuroimaging. However, its success is set back by the lack of large-scale public datasets. Indeed, medical institutions possess a large amount of data; however, open-sourcing is prevented by the legal requirements to protect the patient's privacy.

An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory.

It is unclear to what extent quantum algorithms can outperform classical algorithms for problems of combinatorial optimization. In this work, by resorting to computational learning theory and cryptographic notions, we give a fully constructive proof that quantum computers feature a super-polynomial advantage over classical computers in approximating combinatorial optimization problems. Specifically, by building on seminal work by Kearns and Valiant, we provide special instances that are hard for classical computers to approximate up to polynomial factors.

Understanding quantum machine learning also requires rethinking generalization.

Quantum machine learning models have shown successful generalization performance even when trained with few data. In this work, through systematic randomization experiments, we show that traditional approaches to understanding generalization fail to explain the behavior of such quantum models. Our experiments reveal that state-of-the-art quantum neural networks accurately fit random states and random labeling of training data.

Polymer Waveguide Sensor Based on Evanescent Bragg Grating for Lab-on-a-Chip Applications.

In this work, an evanescent Bragg grating sensor inscribed in a few-mode planar polymer waveguide was integrated into microchannel structures and characterized by various chemical applications. The planar waveguide and the microchannels consisted of epoxide-based polymers. The Bragg grating structure was postprocessed by using point-by-point direct inscription technology.

Optimization of Gate-Head-Top/Bottom Lengths of AlGaN/GaN High-Electron-Mobility Transistors with a Gate-Recessed Structure for High-Power Operations: A Simulation Study.

In this study, we propose an optimized AlGaN/GaN high-electron-mobility transistor (HEMT) with a considerably improved breakdown voltage. First, we matched the simulated data obtained from a basic T-gate HEMT with the measured data obtained from the fabricated device to ensure the reliability of the simulation. Thereafter, to improve the breakdown voltage, we suggested applying a gate-head extended structure.

Highly Sensitive Switchable Sensors for Hydroxide on Glass Surfaces Based on Isoquinolinium-Quinolinium-substituted Acetylenes.

Bi-substituted acetylenes with a quinolinium and an isoquinolinium substituent are described, which reversibly form intensely colored adducts with O-nucleophiles and thus enable the detection of >0,5 ppm hydroxide on the surfaces of various glasses. Acids reconstitute the colorless bi-substituted acetylenes. The quinolinium and isoquinolinium rings are bound via their 2-, 3-, 4- and 1-, 3-, 4-positions to the triple bond, respectively.

Classical Surrogates for Quantum Learning Models.

The advent of noisy intermediate-scale quantum computers has put the search for possible applications to the forefront of quantum information science. One area where hopes for an advantage through near-term quantum computers are high is quantum machine learning, where variational quantum learning models based on parametrized quantum circuits are discussed. In this work, we introduce the concept of a classical surrogate, a classical model which can be efficiently obtained from a trained quantum learning model and reproduces its input-output relations.

The immersive virtual memory task: Assessing object-location memory in neurological patients using immersive virtual reality.

German Clinical Trials Register identifier: DRKS00024005.

Shadow estimation of gate-set properties from random sequences.

With quantum computing devices increasing in scale and complexity, there is a growing need for tools that obtain precise diagnostic information about quantum operations. However, current quantum devices are only capable of short unstructured gate sequences followed by native measurements. We accept this limitation and turn it into a new paradigm for characterizing quantum gate-sets.

Far-UVC- and UVB-induced DNA damage depending on skin type.

Far-UVC radiation sources of wavelengths 222 nm and 233 nm represent an interesting potential alternative for the antiseptic treatment of the skin due to their high skin compatibility. Nevertheless, no studies on far-UVC-induced DNA damage in different skin types have been published to date, which this study aims for. After irradiating the skin with far-UVC of the wavelengths 222 and 233 nm as well as broadband UVB, the tissue was screened for cyclobutane pyrimidine dimer-positive (CPD ) cells using immunohistochemistry.

Data Fusion for Cross-Domain Real-Time Object Detection on the Edge.

We investigate an edge-computing scenario for robot control, where two similar neural networks are running on one computational node. We test the feasibility of using a single object-detection model (YOLOv5) with the benefit of reduced computational resources against the potentially more accurate independent and specialized models. Our results show that using one single convolutional neural network (for object detection and hand-gesture classification) instead of two separate ones can reduce resource usage by almost 50%.

Quantum simulation of thermodynamics in an integrated quantum photonic processor.

One of the core questions of quantum physics is how to reconcile the unitary evolution of quantum states, which is information-preserving and time-reversible, with evolution following the second law of thermodynamics, which, in general, is neither. The resolution to this paradox is to recognize that global unitary evolution of a multi-partite quantum state causes the state of local subsystems to evolve towards maximum-entropy states. In this work, we experimentally demonstrate this effect in linear quantum optics by simultaneously showing the convergence of local quantum states to a generalized Gibbs ensemble constituting a maximum-entropy state under precisely controlled conditions, while introducing an efficient certification method to demonstrate that the state retains global purity.