Quantum metrology in the noisy intermediate-scale quantum era

Quantum metrology pursues the physical realization of higher-precision measurements to physical quantities than the classically achievable limit by exploiting quantum features, such as entanglement and squeezing, as resources. It has potential applications in developing next-generation frequency standards, magnetometers, radar, and navigation. However, the ubiquitous decoherence in the quantum world degrades the quantum resources and forces the precision back to or even worse than the classical limit, which is called the no-go theorem of noisy quantum metrology and greatly hinders its applications. Therefore, how to realize the promised performance of quantum metrology in realistic noisy situations attracts much attention in recent years. We will review the principle, categories, and applications of quantum metrology. Special attention will be paid to different quantum resources that can bring quantum superiority in enhancing sensitivity. Then, we will introduce the no-go theorem of noisy quantum metrology and its active control under different kinds of noise-induced decoherence situations.Comment: Minireview of quantum metrology based on Lectures given at the summer school "Fundamental and Frontiers of Quantum Metrology and Quantum Computation" held in Bohai University, China, from 23 July to 8 Augus

Enhanced surface acceleration of fast electrons by using sub-wavelength grating targets

Surface acceleration of fast electrons in intense laser-plasma interaction is improved by using sub-wavelength grating targets. The fast electron beam emitted along the target surface was enhanced by more than three times relative to that by using planar target. The total number of the fast electrons ejected from the front side of target was also increased by about one time. The method to enhance the surface acceleration of fast electron is effective for various targets with sub-wavelength structured surface, and can be applied widely in the cone-guided fast ignition, energetic ion acceleration, plasma device, and other high energy density physics experiments.Comment: 14 pages, 4figure

Atomic force microscopy measurement of the Young’s modulus and hardness of single LaB6 nanowires

We have employed the atomic force microscopy based (a) three-point bending and (b) nanoindentation methods to obtain the Young’s modulus and hardness of single La B 6 nanowires. The Young’s modulus, E = 467.1 ± 15.8 GPa , is the same as that of the La B 6 single crystals but larger than the sinteredpolycrystalline La B 6 samples. The nanoindentationhardness of the La B 6 nanowire is H = 70.6 ± 2.1 GPa at an indent depth of 4.6 nm , which is higher than that of the La B 6 single crystals, La B 6 polycrystals, and W metals. A superior resistance against thermal vibration, field modification, and ion bombardment is expected for the La B 6 nanowires as a field-emission point electron source

Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning

Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability.Comment: 10 pages, 7 figure

Functional and Transcriptomic Characterization of a Dye-decolorizing Fungus from Taxus Rhizosphere

We isolated three laccase-producing fungus strains from Taxus rhizosphere. Myrotheium verrucaria strain DJTU-sh7 had the highest laccase activity of 216.2 U/ml, which was increased to above 300 U/ml after optimization. DJTU-sh7 had the best decolorizing effect for three classes of reactive dyes. The DJTU-sh7-containing fungal consortium displayed the robust decolorizing ability. Both color removal efficiency and chemical oxygen demand were increased in the consortium mediated biotransformation. Transcriptome changes of M. verrucaria elicited by azo dye and phenolic were quantified by the high throughput transcriptome sequencing, and the activities of the selected oxidases and reductases were determined. The possible involvement of oxidases and reductases, especially laccase, aryl alcohol oxidase, and ferric reductase in the biotransformation of dye and phenolic compounds was revealed at both transcriptomic and phenotypic levels. Revealing the transcriptomic mechanisms of fungi in dealing with organic pollutants facilitates the fine-tuned manipulation of strains in developing novel bioremediation and biodegradation strategies

Ultra-small topological spin textures with size of 1.3nm at above room temperature in Fe78Si9B13 amorphous alloy

Topologically protected spin textures, such as skyrmions1,2 and vortices3,4, are robust against perturbations, serving as the building blocks for a range of topological devices5-9. In order to implement these topological devices, it is necessary to find ultra-small topological spin textures at room temperature, because small size implies the higher topological charge density, stronger signal of topological transport10,11 and the higher memory density or integration for topological quantum devices5-9. However, finding ultra-small topological spin textures at high temperatures is still a great challenge up to now. Here we find ultra-small topological spin textures in Fe78Si9B13 amorphous alloy. We measured a large topological Hall effect (THE) up to above room temperature, indicating the existence of highly densed and ultra-small topological spin textures in the samples. Further measurements by small-angle neutron scattering (SANS) reveal that the average size of ultra-small magnetic texture is around 1.3nm. Our Monte Carlo simulations show that such ultra-small spin texture is topologically equivalent to skyrmions, which originate from competing frustration and Dzyaloshinskii-Moriya interaction12,13 coming from amorphous structure14-17. Taking a single topological spin texture as one bit and ignoring the distance between them, we evaluated the ideal memory density of Fe78Si9B13, which reaches up to 4.44*104 gigabits (43.4 TB) per in2 and is 2 times of the value of GdRu2Si218 at 5K. More important, such high memory density can be obtained at above room temperature, which is 4 orders of magnitude larger than the value of other materials at the same temperature. These findings provide a unique candidate for magnetic memory devices with ultra-high density.Comment: 26 pages, 4 figure

An Effective Satellite Remote Sensing Tool Combining Hardware and Software Solutions

In this paper we propose a new effective remote sensing tool combining hardware and software solutions as an extension of our previous work. In greater detail the tool consists of a low cost receiver subsystem for public weather satellites and a signal and image processing module for several tasks such as signal and image enhancement, image reconstruction and cloud detection. Our solution allows to manage data from satellites effectively with low cost components and portable software solutions. We aim at sampling and processing of the modulated signal entirely in software enabled by Software Defined Radios (SDR) and CPU computational speed overcoming hardware limitation such as high receiver noise and low ADC resolution. Since we want to extend our previous method to demodulate signals coming from various meteorological satellites, we propose a new high frequency receiving system designed to receive and demodulate signals transmitted at 1.7 GHz. The signals coming from satellites are demodulated, synchronized and enhanced by using low level image processing techniques, then cloud detection is performed by using the well known K-means clustering algorithm. The hardware and software architecture extensions make our solution able to receive and demodulate high frequency and bandwidth meteorological satellite signals, such as those transmitted by NOAA POES, NOAA GOES, EUMETSAT Metop, Meteor-M and FengYun

Qwen Technical Report

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.Comment: 59 pages, 5 figure