Hadi Salloum

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Verified

Hadi verified their affiliation via an institutional email.

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• BSc student in Data Science and Artificial Intelligence
• Researcher at Innopolis University

The research aims to advance the understanding of Large Language Models (LLMs) by examining their ability to predict the lexical complexity of words within contextualized sentences. A multifaceted approach is employed, beginning with prompt engineering to assess model performance autonomously (Zero-Shot) and with minimal supplementary information (...

This study introduces a novel application of Quantum Generative Adversarial Networks (QGANs) by incorporating a new fairness principle, representational fairness, which improves equitable representation of various demographic groups in quantum-generated data. We propose a group-wise gradient norm clipping technique that constrains the magnitude of...

Quantum Annealing (QA) on D-Wave's Advantage system and Tensor Train (TT) sampling are compared for QUBO-based ADMET classification. QA-based methods (QSVM, QBoost) leverage quantum effects to escape local minima, while TT sampling employs low-rank decompositions for efficient high-dimensional data handling. Benchmarks highlight TT sampling's poten...

We propose TT-Seg, an unsupervised image segmentation framework that employs Tensor Train (TT) decomposition and probabilistic tensor sampling to optimize Quadratic Unconstrained Binary Optimization (QUBO) problems. TT-Seg achieves segmentation performance comparable to classical solvers while offering enhanced scalability. Experimental results ind...

As industries increasingly encounter complex optimization challenges, the convergence of Artificial Intelligence (AI) and Quantum Computing offers a promising pathway to scalable, efficient solutions. This work proposes a novel framework-including the introduction of Q GPT, a purpose-built AI system designed to solve complex optimization problems u...

This paper explores the application of quantum annealing to solve the inverse kinematics (IK) problem in robotics by reformulating it as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Using a linear binary approximation of trigonometric functions, we transform the IK problem into a form suitable for quantum optimization, demonstratin...

Children with Autism Spectrum Disorder (ASD) often struggle with conventional education due to challenges in social communication and sensory sensitivities. However, many possess exceptional cognitive abilities that remain underutilized in traditional learning environments. This paper explores the potential of Large Language Models (LLMs) as adapti...

As industries increasingly encounter complex optimization challenges, the convergence of Artificial Intelligence (AI) and Quantum Computing offers a promising pathway to scalable, efficient solutions. This work proposes a novel framework — including the introduction of Q GPT, a purpose-built AI system designed to solve complex optimization problems...

This paper presents a novel solution aimed at enhancing news web pages for seamless integration into the Semantic Web. By utilizing advanced pattern mining techniques alongside OpenAI’s GPT-3, we rewrite news articles to improve their readability and accessibility for Google News aggregators. Our approach is characterized by its methodological rigo...

Irrelevant elements like ads, menus, and footers in web pages hinder data extraction and reduce the performance of Retrieval-Augmented Generation (RAG) systems in Large Language Models (LLMs). This paper tackles the challenge of accurately identifying and extracting the main content from web pages to enhance the efficiency of these systems. We pres...

The Quantum Annealer built by D-Wave, known as Advantage, is currently the largest quantum computer in the world, featuring a topology called "Pegasus." This groundbreaking system opens new possibilities for solving highly complex problems. The advancement of quantum annealers has spurred experimental demonstrations and intensified research interes...

The rapid expansion of the non-fungible token (NFT) market has opened new avenues for artists, collectors, and investors. However, it has simultaneously exposed significant vulnerabilities related to the storage, verification, and distribution of associated metadata. This paper provides an in-depth examination of the existing NFT metadata storage l...

The Quantum Annealer built by D-Wave, known as Advantage, is currently the largest quantum computer in the world, featuring a topology called "Pegasus." This groundbreaking system opens new possibilities for solving highly complex problems. The advancement of quantum annealers has spurred experimental demonstrations and intensified research interes...

In this paper, we examine the use of quantum annealing for the Traveling Salesman Problem (TSP) using the D-Wave Advantage quantum annealer and its "Pegasus" architecture. We introduce a refined Quadratic Unconstrained Binary Optimization (QUBO) formulation that simplifies the problem by eliminating the first node and reallocating its effect, there...

A Large Language Model (LLM) is an advanced artificial intelligence algorithm that utilizes deep learning methodologies and extensive datasets to process, understand, and generate human-like text. These models are capable of performing various tasks, such as summarization, content creation, translation, and predictive text generation, making them h...

This paper presents a novel solution aimed at enhancing news web pages for seamless integration into the Semantic Web. By utilizing advanced pattern mining techniques alongside OpenAI’s GPT-3, we rewrite news articles to improve their readability and accessibility for Google News aggregators. Our approach is characterized by its methodological rigo...

Traffic congestion continues to pose a significant challenge in urban environments, necessitating innovative approaches to traffic management. This paper explores the application of Quantum Annealing (QA) for real-world traffic optimization, expanding on the pioneering work of Volkswagen and D-Wave. In 2017, a collaborative team demonstrated the po...

This study addresses the dynamic object detection problem for Unmanned Surface Vehicles (USVs) in marine environments, which is complicated by boat tilting and camera illumination sensitivity. A novel pipeline named “Seal” is proposed to enhance detection accuracy and reliability. The approach begins with an innovative preprocessing stage that inte...

Research on measuring attention via EEG during cognitive tasks has been extensive, yet the relationship between attention levels in programming and software bug frequency remains underexplored. Debugging is a central, time-intensive phase in software development that often delays project timelines due to the challenges of detecting and resolving er...

Photonic neural networks (PNNs) represent a promising intersection of optical computing and artificial intelligence, offering potential advancements in speed and energy efficiency. This paper explores the core architecture of PNNs, their underlying principles, and the method-ologies for implementing software-defined neural networks onto photonic pl...

Quantum annealers are entering a new era of technology. The "Advantage 1" quantum annealer, developed by D-Wave Systems Inc. with the "Pegasus" topology, is currently the largest quantum computer in the world. Quantum annealing, a specialized variant of simulated annealing that utilizes quantum properties, offers a wide array of opportunities to ad...

Despite extensive research on attention measurement via EEG during cognitive tasks, the specific relationship between attention levels in programming and software bug frequency remains largely unexplored. Debugging, a critical and time-consuming part of software development, often causes delays due to the effort needed to identify and resolve error...

This work explores the application of quantum annealing to enhance robotic manipulation systems. Specifically, we aim to theoretically illustrate how QA can be employed to optimize robotic manipulation tasks. By formulating problems using either Quadratic Unconstrained Binary Optimization or Ising models, we introduce the Fully Integrated Quantum A...

In this paper, we examine the use of quantum annealing for the Traveling Salesman Problem (TSP) using the D-Wave Advantage quantum annealer and its "Pegasus" architecture. We introduce a refined Quadratic Unconstrained Binary Optimization (QUBO) formulation that simplifies the problem by eliminating the first node and reallocating its effect, there...

Classical computers face significant challenges when dealing with NP problems, especially given the unresolved question of whether NP equals P. These challenges arise due to the computational complexity and resource limitations inherent in solving such problems efficiently. Quantum computing, on the other hand, shows promise in addressing these cha...

Photonic neural networks (PNNs) represent a promising intersection of optical computing and artificial intelligence, offering potential advancements in speed and energy efficiency. This paper explores the core architecture of PNNs, their underlying principles, and the methodologies for implementing software-defined neural networks onto photonic pla...

Unmanned aerial vehicles (UAVs) represent a fast growing industry with great potential. UAVS find applications in various fields for both military and civilian purposes. For example, they are increasingly used for transportation as: private vehicles, vehicles for public transport, and/or for the transport of goods. This work aimed at reviewing, com...

The greatest accomplishments do not arise from only effort, but from the genesis of a profound idea. This idea, born from the vision of individuals driven by a strong desire to foster progress and improvement, forms the foundation upon which great edifices are built. In an era marked by unprecedented global uncertainty, this paper seeks to illumina...

This work focuses on the application of quantum annealing to the mechanics of robot inspectors used for electrical transmission line maintenance. The integration of quantum annealing in robotics enables the resolution of NP-complete problems and the efficient solving of differential equations that describe the dynamics of robot inspectors. The math...

Quantum computing (QC) has become a fascinating and popular topic due to its broad range of applications, particularly in machine learning (ML). The intersection of QC and ML is known as Quantum Machine Learning (QML). QML is a field that investigates how QC can enhance ML, making it one of the most exciting areas of research due to the potential o...

This paper conducts an exhaustive exploration of the evolutionary journey of microservices within the domain of software architecture. It meticulously traces the historical trajectory, current status, and potential future pathways of microservices in software design. Additionally, this study introduces a pioneering concept known as Quantum Microser...

The hasty advancement of Quantum Computing (QC) technologies has led to offer plethora of promising offering promising opportunities for solving complex optimization problems, especially Quantum Annealing (QA). This paper presents an investigation into the fusion of ML and QA. It also explores and elucidates the capabilities of ML empowered by QA t...

This paper conducts an exhaustive exploration of the evolutionary journey of microservices within the domain of software architecture. It meticulously traces the historical trajectory, current status, and potential future pathways of microservices in software design. Additionally, this study introduces a pioneering concept known as Quantum Microser...

This paper presents a exploration into the integration of Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) within networking in-frastructures, marking a groundbreaking advancement in network security. It meticulously examines the vulnerabilities inherent in classical and post-quantum cryptographic methods, underlining the pressing...

This paper comprehensively investigates collision conditions in three-body problems, incorporating General Relativity (GR) effects. The study analyzes the initial values of the bodies to determine the collision possibility and develops a high-accuracy machine learning model for classifying collision events. The study introduces the concept of GR-ef...

Quantum Computing has emerged as a transformative technology, offering solutions to complex problems across various domains. Leading this innovation is Leap™ by D-Wave Systems, offering Quantum Computing as a Service (QCaaS) with an Integrated Development Environment (IDE) tailored for quantum programming. Recently, the Leap™ IDE transitioned to Gi...

In recent years, the exploration of innovative perspectives within quantum field theories has emerged as a central focus in the domain of mathematical modeling for the physical sciences. The papers submitted for consideration in the 13th International Conference on Mathematical Modeling in Physical Sciences represent a diverse array of pioneering a...

The rapid advancement of quantum computing technologies has led to an intersection with methodologies in machine learning, offering promising avenues for solving complex optimization problems. This paper presents an innovative investigation into the fusion of machine learning and quantum annealing. It explores the enhanced capabilities of machine l...

This paper presents a exploration into the integration of Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) within networking infrastructures, marking a groundbreaking advancement in network security. It meticulously examines the vulnerabilities inherent in classical and post-quantum cryptographic methods, underlining the pressing...

This paper conducts an exhaustive exploration of the evolutionary journey of microservices within the domain of software architecture. It meticulously traces the historical trajectory, current status, and potential future pathways of microservices in software design. Additionally, this study introduces a pioneering concept known as Quantum Microser...

Reinforcement Learning (RL) seeks to develop systems capable of autonomous decision-making by learning through interaction with their environment. Central to this process are reward engineering and reward shaping, which are essential for enhancing the efficiency and effectiveness of RL algorithms. These techniques guide agents toward desired behavi...

This presentation delves deeply into the collision dynamics that arise in scenarios involving three bodies, while also considering the influence of General Relativity. The primary focus is on analyzing the initial conditions of these celestial bodies and assessing the likelihood of collisions. The presentation introduces an advanced machine learnin...

This paper comprehensively investigates collision conditions in three-body problems, incorporating General Relativity (GR) effects. The study analyzes the initial values of the bodies to determine the collision possibility and develops a high-accuracy machine learning model for classifying collision events. The study introduces the concept of GR-ef...