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arXiv 2024年

作者： Marini, I. Saro, A. Borgani, S. Boi, M. European Southern Observatory Karl Schwarzschildstrasse 2 Garching bei München85748 Germany Dipartimento di Fisica - Sezione di Astronomia Università di Trieste Via Tiepolo 11 Trieste34131 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34151 Italy INFN Sezione di Trieste Via Valerio 2 Trieste34127 Italy ICSC - Italian Research Center on High-Performance Computing Big Data and Quantum Computing Italy

Context. In the context of structure formation, disentangling the central galaxy stellar population from the stellar intrahalo light can help us shed light on the formation history of the halo as a whole, as the properties of the stellar components are expected to retain traces of the formation history. Many approaches are adopted to assess the task, depending on different physical assumptions (e.g. the light profile, chemical composition, and kinematical differences) and depending on whether the full six-dimensional phase-space information is known (much like in simulations) or whether one analyses projected quantities (i.e. observations). Aims. This paper paves the way for a new approach to bridge the gap between observational and simulation methods. We propose the use of projected kinematical information from stars in simulations in combination with deep learning to create a robust method for identifying intrahalo light in observational data to enhance understanding and consistency in studying the process of galaxy formation. Methods. Using deep learning techniques, particularly a convolutional neural network called U-Net, we developed a methodology for predicting these contributions in simulated galaxy cluster images. We created a sample of mock images from hydrodynamical simulations (including masking of the interlopers) to train, validate and test the network. Reinforced training (Attention U-Net) was used to improve the first results, as the innermost central regions of the mock images consistently overestimate the stellar intrahalo contribution. Results. Our work shows that adequate training over a representative sample of mock images can lead to good predictions of the intrahalo light distribution. The model is mildly dependent on the training size and its predictions are less accurate when applied to mock images from different simulations. However, the main features (spatial scales and gradients of the stellar fractions) are recovered for all tests. While

关键词： Forecasting

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JERALD: high-fidelity dark matter, stellar mass and neutral hydrogen maps from fast N-body simulations

arXiv

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arXiv 2025年

作者： Rigo, Mauro Trotta, Roberto Viel, Matteo Theoretical and Scientific Data Science SISSA Via Bonomea 265 Trieste34136 Italy INFN National Institute for Nuclear Physics Via Valerio 2 Trieste34127 Italy ICSC Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Astrophysics Group Physics Department Blackett Lab Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom INAF Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34143 Italy

We present a new code and approach, JERALD—JAX Enhanced Resolution Approximate Lagrangian Dynamics—, that improves on and extends the Lagrangian Deep Learning method of Dai & Seljak (2021), producing high-resolution dark matter, stellar mass and neutral hydrogen maps from lower-resolution approximate N-body simulations. The model is trained using the Sherwood-Relics simulation suite (for a fixed cosmology), specifically designed for the intergalactic medium and the neutral hydrogen distribution in the cosmic web. The output is tested in the redshift range from z = 5 to z = 0 and the generalization properties of the learned mapping is demonstrated. JERALD produces maps with dark matter, stellar and neutral hydrogen power spectra in excellent agreement with full-hydrodynamic simulations with 8× higher resolution, at large and intermediate scales;in particular, JERALD’s neutral hydrogen power spectra agree with their higher-resolution full-hydrodynamic counterparts within 90% up to k 1 hMpc−1 and within 70% up to k 10 hMpc−1. JERALD provides a fast, accurate and physically motivated approach that we plan to embed in a statistical inference pipeline, such as Simulation-Based Inference, to constrain dark matter properties from large- to intermediate-scale structure observables. Copyright © 2025, The Authors. All rights reserved.

关键词： Dark Matter

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Propensity of oxidation of transition metal dichalcogenide monolayers in relation to physical configuration of chalcogen vacancies

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Physical Review B 2025年第9期111卷 094107-094107页

作者： Hiroyo Kawai Fabio Bussolotti Khoong Hong Khoo Kuan Eng Johnson Goh ASTAR Quantum Innovation Centre (Q.InC) Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (ASTAR) 1 Fusionopolis Way 16-16 Connexis Singapore 138632 Republic of Singapore ASTAR Quantum Innovation Centre (Q.InC) Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (ASTAR) 2 Fusionopolis Way 08-03 Innovis Singapore 138634 Republic of Singapore Department of Physics National University of Singapore 2 Science Drive 3 Singapore 117551 Republic of Singapore Department of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore 637371 Republic of Singapore

Using first-principles calculations, we studied how the physical configuration of chalcogen vacancies impact the initial step of surface oxidation of two-dimensional transition metal dichalcogenides (TMDs) where the O2 molecule is adsorbed and dissociated into two O atoms. The effects of surface defects on oxidation are studied by calculating the adsorption energies of the O2 molecule and O atoms on the surface as a function of defect size. We showed that the adsorption of O2 on the surface with one or more chalcogen vacancies is thermodynamically more favorable compared to the pristine surface. Furthermore, the stability of adsorption of two O atoms increases with the number of chalcogen vacancies. The calculated projected density of states indicates that the dangling bond (in-gap) states created by the chalcogen vacancies are passivated by the adsorption of O atoms. Oxygen adsorption kinetics calculation shows that there exists a significant energy barrier of about 1 eV for the dissociation of O2 on a surface with only single chalcogen vacancy, whereas a substantially reduced barrier is observed for a surface with two or more vacancies. Our findings suggest that even though vacancy-free two-dimensional (2D) TMDs might not be practically available, the 2D TMD crystals with mainly single chalcogen vacancy defects should be resistant against oxidation and thus provide critical guidance to researchers regarding optimizing the density and clustering of chalcogen vacancies during the crystal growth.

关键词： Dangling bonds

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Graph adversarial self-supervised learning 21

Graph adversarial self-supervised learning

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Longqi Yang Liangliang Zhang Wenjing Yang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China and Defense Innovation Institute Beijing China Institute of Systems Engineering AMS Beijing China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9781713845393

This paper studies a long-standing problem of learning the representations of a whole graph without human supervision. The recent self-supervised learning methods train models to be invariant to the transformations (views) of the inputs. However, designing these views requires the experience of human experts. Inspired by adversarial training, we propose an adversarial self-supervised learning (GASSL) framework for learning unsupervised representations of graph data without any handcrafted views. GASSL automatically generates challenging views by adding perturbations to the input and are adversarially trained with respect to the encoder. Our method optimizes the min-max problem and utilizes a gradient accumulation strategy to accelerate the training process. Experimental on ten graph classification datasets show that the proposed approach is superior to state-of-the-art self-supervised learning baselines, which are competitive with supervised models.

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On the Robustness of Cluster Clustering Covariance Calibration

arXiv

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arXiv 2025年

作者： Fumagalli, A. Castro, T. Borgani, S. Valentini, M. Ludwig-Maximilians-University Schellingstrasse 4 Munich80799 Germany INAF - Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Dipartimento di Fisica – Sezione di Astronomia Università di Trieste Via Tiepolo 11 Trieste34131 Italy

Ongoing and upcoming wide-field surveys at different wavelengths will measure the distribution of galaxy clusters with unprecedented precision, demanding accurate models for the two-point correlation function (2PCF) covariance. In this work, we assess a semi-analytical framework for the cluster 2PCF covariance that employs three nuisance parameters to account for non-Poissonian shot noise, residual uncertainties in the halo bias model, and subleading noise terms. We calibrate these parameters on a suite of fast approximate simulations generated by PINOCCHIO as well as full N-body simulations from OpenGADGET3. We demonstrate that PINOCCHIO can reproduce the 2PCF covariance measured in OpenGADGET3 at the few percent level, provided the mass functions are carefully rescaled. Resolution tests confirm that high particle counts are necessary to capture shot-noise corrections, especially at high redshifts. We perform the parameter calibration across multiple cosmological models, showing that one of the nuisance parameters, the non-Poissonian shot-noise correction α, depends mildly on the amplitude of matter fluctuations σ8. In contrast, the remaining two parameters, β controlling the bias correction and γ controlling the secondary shot-noise correction, exhibit more significant variation with redshift and halo mass. Overall, our results underscore the importance of calibrating covariance models on realistic mock catalogs that replicate the selection function of forthcoming surveys and highlight that approximate methods, when properly tuned, can effectively complement full N-body simulations for precision cluster cosmology. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmology

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On-chip multiphoton Greenberger-Horne Zeilinger state based on integrated frequency combs

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Frontiers of physics 2020年第6期15卷 33-41页

作者： Pingyu Zhu Qilin Zheng Shichuan Xue Chao Wu Xinyao Yu Yang Wang Yingwen Liu Xiaogang Qiang Junjie Wu Ping Xu Institute for Quantum Information and State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China National Innovation Institute of Defense Technology AMSBeijing 100071China National Laboratory of Solid State Micro structures and School of Physics Nanjing UniversityNanjing 210093China

One of the most important multipartite entangled states,Greenberger-Horne-Zeilinger state(GHZ),serves as a fundamental resource for quantum foundat ion test,quantum communication and quantum *** increase the number of entangled particles,significant experimental efforts should been invested due to the complexity of optical setup and the difficulty in maintaining the coherence condition for high-fidelity GHZ ***,we propose an ultra-integrated scalable on-chip GHZ state generation scheme based on frequency *** designing several microrings pumped by different lasers,multiple partially overlapped quantum frequency combs are generated to supply as the basis for on-chip polarization-encoded GHZ state with each qubit occupying a certain spectral *** even and odd numbers of GHZ states can be engineered with constant small number of integrated components and easily scaled up on the same chip by only adjusting one of the pumnp *** addition,we give the on-chip design of projection measurement for characterizing GHZ states and show the reconfigurability of the *** proposal is rather simple and feasible within the existing fabrication technologies and we believe it will boost the development of multiphoton technologies.

关键词： quantum information Greenberger-Horne Zeilinger state frequency comb

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Bayesian evidence estimation from posterior samples with normalizing flows

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Physical Review D 2024年第12期110卷 123007-123007页

作者： Rahul Srinivasan Marco Crisostomi Roberto Trotta Enrico Barausse Matteo Breschi SISSA Via Bonomea 265 34136 Trieste Italy INFN Sezione di Trieste 34149 Trieste Italy IFPU - Institute for Fundamental Physics of the Universe Via Beirut 2 34014 Trieste Italy TAPIR Division of Physics Mathematics and Astronomy California Institute of Technology Pasadena California 91125 USA Dipartimento di Fisica Università di Pisa Largo Bruno Pontecorvo 3 56127 Pisa Italy Department of Physics Imperial College London Blackett Lab Prince Consort Rd SW7 2AZ London United Kingdom Italian Research Center on High Performance Computing Big Data and Quantum Computing 13 Via Magnanelli 2 – 40033 Italy

We propose a novel method (floz), based on normalizing flows, to estimate the Bayesian evidence (and its numerical uncertainty) from a preexisting set of samples drawn from the unnormalized posterior distribution. We validate it on distributions whose evidence is known analytically, up to 15 parameter space dimensions, and compare with two state-of-the-art techniques for estimating the evidence: nested sampling (which computes the evidence as its main target) and a k-nearest-neighbors technique that produces evidence estimates from posterior samples. Provided representative samples from the target posterior are available, our method is more robust to posterior distributions with sharp features, especially in higher dimensions. For a simple multivariate Gaussian, we demonstrate its accuracy for up to 200 dimensions with 105 posterior samples. floz has wide applicability, e.g., to estimate evidence from variational inference, Markov chain Monte Carlo samples, or any other method that delivers samples and their likelihood from the unnormalized posterior density. As a physical application, we use floz to compute the Bayes factor for the presence of the first overtone in the ringdown signal of the gravitational wave data of GW150914, finding good agreement with nested sampling.

关键词： Gravitational waves Probabilistic computing Artificial neural networks Astronomical black holes Network inference

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanlong Qiu Siqi Wang Xi Yang Xinyuan Qiu Chengkun Wu Yingbo Cui Canqun Yang Institute for Quantum Information & State Key Laboratory of High-Performance Computing College of Computer Science National University of Defense Technology Changsha P. R. China National Supercomputer Center in Tianjin Tianjin P. R. China National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha P. R. China Department of Biology and Chemistry College of Science National University of Defense Technology Changsha P. R. China

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

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A Max K-Cut Implementation for QAOA in the Measurement Based quantum computing Formalism

A Max K-Cut Implementation for QAOA in the Measurement Based...

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quantum computing and Engineering (QCE), IEEE International Conference on

作者： Sebastiano Corli Daniele Dragoni Massimiliano Proietti Massimiliano Dispenza Carlo Cavazzoni Enrico Prati Dipartimento di Fisica Politecnico di Milano Milan Italy Istituto di Fotonica e Nanotecnologia CNR Milan Italy High Performance Computing Laboratory Leonardo s.p.a Genova Italy Quantum Technologies Leonardo Lab Leonardo s.p.a Rome Italy Dipartimento di Fisica Università degli Studi di Milano Milan Italy

The MaxCut is a combinatorial problem which belongs to the NP-hard class. The algorithm to solve it can be mapped into a quantum system, where the optimal configuration matches the ground state of a target Hamiltonian. It is possible to implement the adiabatic theorem into the circuit model via the QAOA algorithm, by arbitrarily choosing from both the gate-based and the measurement-based quantum computing (MBQC) settings. Recently, the MaxCut problem has been generalized to the Max K-Cut version also in the MBQC frame, where K stands for the number of cuts to implement. Here, the QAOA algorithm for a Max K-Cut is solved for a graph with arbitrary number of vertices V and number of cuts K scaling as a power of 2, along with the circuit depth p. The problem is solved on a emulator based on classical HPC resources. In order to test the performances of a Max K-cut implemented in the MBQC frame, we report how the average runtime and the approximation ratio scale by increasing V, K and p, comparing the results from two different topologies of graphs, 2-regular graphs with null weights on the diagonals versus the the fully-connected graphs. Eventually, the approximation ratio, evaluated as a parameter of success, is shown to yield optimal performances.

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Intensity correlations in decoy-state BB84 quantum key distribution systems

arXiv

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arXiv 2024年

作者： Trefilov, Daniil Sixto, Xoel Zapatero, Víctor Huang, Anqi Curty, Marcos Makarov, Vadim Vigo Quantum Communication Center University of Vigo VigoE-36310 Spain School of Telecommunication Engineering Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain atlanTTic Research Center University of Vigo VigoE-36310 Spain Russian Quantum Center Skolkovo Moscow121205 Russia National Research University Higher School of Economics Moscow101000 Russia Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

The decoy-state method is a prominent approach to enhance the performance of quantum key distribution (QKD) systems that operate with weak coherent laser sources. Due to the limited transmissivity of single photons in optical fiber, current experimental decoy-state QKD setups increase their secret key rate by raising the repetition rate of the transmitter. However, this usually leads to correlations between subsequent optical pulses. This phenomenon leaks information about the encoding settings, including the intensities of the generated signals, which invalidates a basic premise of decoy-state QKD. Here we characterize intensity correlations between the emitted optical pulses in two industrial prototypes of decoy-state BB84 QKD systems and show that they significantly reduce the asymptotic key rate. In contrast to what has been conjectured, we experimentally confirm that the impact of higher-order correlations on the intensity of the generated signals can be much higher than that of nearest-neighbour correlations. Copyright © 2024, The Authors. All rights reserved.

关键词： quantum cryptography

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