Search

This is a brief note on a recently flagged issue \cite{CHK} regarding the precise characterization of quadrupolar truncation of the linearized gravitational waves in de Sitter space-time. An apparent inconsistency was noted while transforming the linearized solution truncated to quadrupolar contribution to the Bondi-Sachs form. A consistent truncation was identified as one where the transformation of the truncated solution does not generate logarithmic terms inadmissible in the Bondi-Sachs asymptotic form. I point out the role of the gauge conditions and the solution of the homogeneous equation in obviating the apparent inconsistency issue., Comment: 13 pages, no figures

In a well-ordered crystalline solid, insulating behaviour can arise from two mechanisms: electrons can either scatter off a periodic potential, thus forming band gaps that can lead to a band insulator, or they localize due to strong interactions, resulting in a Mott insulator. For an even number of electrons per unit cell, either band- or Mott-insulators can theoretically occur. However, unambiguously identifying an unconventional Mott-insulator with an even number of electrons experimentally has remained a longstanding challenge due to the lack of a momentum-resolved fingerprint. This challenge has recently become pressing for the layer dimerized van der Waals compound Nb$_3$Br$_8$, which exhibits a puzzling magnetic field-free diode effect when used as a weak link in Josephson junctions, but has previously been considered to be a band-insulator. In this work, we present a unique momentum-resolved signature of a Mott-insulating phase in the spectral function of Nb$_3$Br$_8$: the top of the highest occupied band along the out-of-plane dimerization direction $k_z$ has a momentum space separation of $\Delta k_z=2\pi/d$, whereas the valence band maximum of a band insulator would be separated by less than $\Delta k_z=\pi/d$, where $d$ is the average spacing between the layers. As the strong electron correlations inherent in Mott insulators can lead to unconventional superconductivity, identifying Nb$_3$Br$_8$ as an unconventional Mott-insulator is crucial for understanding its apparent time-reversal symmetry breaking Josephson diode effect. Moreover, the momentum-resolved signature employed here could be used to detect quantum phase transition between band- and Mott-insulating phases in van der Waals heterostructures, where interlayer interactions and correlations can be easily tuned to drive such transition., Comment: 9 pages, 3 figures

We show that in some transition metal dichalcogenides, minority regions of the cleaved sample surfaces show - unexpectedly and anomalously - a finite number of 2D electronic states instead of the expected 3D valence bands. In the case of NbS\textsubscript{2}, in addition to the typical spectrum associated with bulk 2Ha stacking, we also find minority regions with electronic structures consistent with few-layers of 3R stacking. In MoS\textsubscript{2} we find areas of both bulk 2Hc and 3R stackings, and regions exhibiting finite-layer quantisation of both types. We further find evidence for a more exotic 4Ha stacking of MoS\textsubscript{2}, in which the valence band maximum is quasi-2D. The results highlight how variation of the interlayer stacking of van der Waals materials beyond the commonly-reported bulk polytypes can yield novel electronic structures.

Recent advancements in real-time object detection frameworks have spurred extensive research into their application in robotic systems. This study provides a comparative analysis of YOLOv5 and YOLOv8 models, challenging the prevailing assumption of the latter's superiority in performance metrics. Contrary to initial expectations, YOLOv5 models demonstrated comparable, and in some cases superior, precision in object detection tasks. Our analysis delves into the underlying factors contributing to these findings, examining aspects such as model architecture complexity, training dataset variances, and real-world applicability. Through rigorous testing and an ablation study, we present a nuanced understanding of each model's capabilities, offering insights into the selection and optimization of object detection frameworks for robotic applications. Implications of this research extend to the design of more efficient and contextually adaptive systems, emphasizing the necessity for a holistic approach to evaluating model performance., Comment: 11th IEEE International Conference on Cybernetics and Intelligent Systems (CIS)

Adiabatic quantum computers can solve difficult optimization problems (e.g., the quadratic unconstrained binary optimization problem), and they seem well suited to train machine learning models. In this paper, we describe an adiabatic quantum approach for training support vector machines. We show that the time complexity of our quantum approach is an order of magnitude better than the classical approach. Next, we compare the test accuracy of our quantum approach against a classical approach that uses the Scikit-learn library in Python across five benchmark datasets (Iris, Wisconsin Breast Cancer (WBC), Wine, Digits, and Lambeq). We show that our quantum approach obtains accuracies on par with the classical approach. Finally, we perform a scalability study in which we compute the total training times of the quantum approach and the classical approach with increasing number of features and number of data points in the training dataset. Our scalability results show that the quantum approach obtains a 3.5--4.5 times speedup over the classical approach on datasets with many (millions of) features.

In many real-world datasets, rows may have distinct characteristics and require different modeling approaches for accurate predictions. In this paper, we propose an adaptive modeling approach for row-type dependent predictive analysis(RTDPA). Our framework enables the development of models that can effectively handle diverse row types within a single dataset. Our dataset from XXX bank contains two different risk categories, personal loan and agriculture loan. each of them are categorised into four classes standard, sub-standard, doubtful and loss. We performed tailored data pre processing and feature engineering to different row types. We selected traditional machine learning predictive models and advanced ensemble techniques. Our findings indicate that all predictive approaches consistently achieve a precision rate of no less than 90%. For RTDPA, the algorithms are applied separately for each row type, allowing the models to capture the specific patterns and characteristics of each row type. This approach enables targeted predictions based on the row type, providing a more accurate and tailored classification for the given dataset.Additionally, the suggested model consistently offers decision makers valuable and enduring insights that are strategic in nature in banking sector.

This research explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms, aiming to assess the performance enhancements and computational implications across a spectrum of models. We explore various classical-to-quantum mapping methods, ranging from basis encoding, angle encoding to amplitude encoding for encoding classical data, we conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead. This study underscores the potential of quantum data embedding in enhancing classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research directions may involve refining quantum encoding processes to optimize computational efficiency and exploring scalability for real-world applications. Our work contributes to the growing body of knowledge at the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios.

Machine Learning (ML) models are trained using historical data to classify new, unseen data. However, traditional computing resources often struggle to handle the immense amount of data, commonly known as Big Data, within a reasonable time frame. Quantum Computing (QC) provides a novel approach to information processing, offering the potential to process classical data exponentially faster than classical computing through quantum algorithms. By mapping Quantum Machine Learning (QML) algorithms into the quantum mechanical domain, we can potentially achieve exponential improvements in data processing speed, reduced resource requirements, and enhanced accuracy and efficiency. In this article, we delve into both the QC and ML fields, exploring the interplay of ideas between them, as well as the current capabilities and limitations of hardware. We investigate the history of quantum computing, examine existing QML algorithms, and present a simplified procedure for setting up simulations of QML algorithms, making it accessible and understandable for readers. Furthermore, we conduct simulations on a dataset using both traditional machine learning and quantum machine learning approaches. We then compare their respective performances by utilizing a quantum simulator.

This document presents a summary of the 2023 Terrestrial Very-Long-Baseline Atom Interferometry Workshop hosted by CERN. The workshop brought together experts from around the world to discuss the exciting developments in large-scale atom interferometer (AI) prototypes and their potential for detecting ultralight dark matter and gravitational waves. The primary objective of the workshop was to lay the groundwork for an international TVLBAI proto-collaboration. This collaboration aims to unite researchers from different institutions to strategize and secure funding for terrestrial large-scale AI projects. The ultimate goal is to create a roadmap detailing the design and technology choices for one or more km-scale detectors, which will be operational in the mid-2030s. The key sections of this report present the physics case and technical challenges, together with a comprehensive overview of the discussions at the workshop together with the main conclusions., Comment: Summary of the Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at CERN: https://indico.cern.ch/event/1208783/

Quantum Computers offer an intriguing challenge in modern Computer Science. With the inevitable physical limitations to Moore's Law, quantum hardware provides avenues to solve grander problems faster by utilizing Quantum Mechanical properties at subatomic scales. These futuristic devices will likely never replace traditional HPC, but rather work alongside them to perform complex tasks, utilizing the best of decades of HPC and quantum computing research. We leverage the capabilities of scientific workflows to make traditional HPC and Quantum Computers work together. To demonstrate this capability, we implemented three algorithms: Grover's Search Algorithm, Shor's Factoring Algorithm, and a 4-node Traveling Salesman Algorithm. The algorithms' implementation and generated inputs are sent from ORNL HPC to IBMQ, the algorithms run on IBMQ, and the results return. The entire process is automated as a workflow by encoding it into the Parsl parallel scripting and workflow platform., Comment: 15 pages, 8 figures

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network., Comment: Manuscript accepted at ICONS'23