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Francesco D'agostino

Francesco D'agostino

Physicist | AI & Quantum Computing Engineer | Penetration Tester |

Italy flagPalermo, Italy
$30.00/hrExpertInternal Proprietary ToolingDon T Disclose

Key Skills

Software

Internal/Proprietary Tooling
Don't disclose

Top Subject Matter

Scientific Analysis
From-Scratch Algorithm Development
Predictive/Forecasting Models

Top Data Types

3D Sensor
Computer Code ProgrammingComputer Code Programming
Medical DicomMedical Dicom

Top Task Types

Classification
Computer Programming Coding
Data Collection
Entity Ner Classification
Evaluation Rating

Freelancer Overview

I am an incoming university student with a deep passion for physics and mathematics, having already published extensively in these areas. As a physicist and mathematician, I have worked on cutting-edge projects in quantum computing and AI, focusing on integrating classical methods with AI models and applying data science in both physical and biochemical research. I actively contribute to open-source projects, write on Medium, and participate frequently on Mathematics Stack Exchange. Additionally, I possess significant expertise in cybersecurity, specializing in both offensive and defensive strategies.

ExpertArabicFrenchGermanPashtoEnglishItalianSpanish

Labeling Experience

Quantum Holographic ML Black Hole Information Paradox Resolution

Internal Proprietary ToolingComputer Code ProgrammingBounding BoxPolygon
This project explores the black hole information paradox by integrating quantum computing, holographic entanglement entropy, and black hole thermodynamics. I employed variational quantum eigensolvers (VQEs) to model black hole dynamics and compute holographic entanglement entropy (HEE) using Schwarzschild-AdS and Reissner-Nordström-AdS metrics. By simulating Hawking radiation and black hole evaporation, I analyzed the entropy evolution and assess information preservation. The project addresses key challenges such as metric singularities, numerical integration issues, and entropy calculation discrepancies. These findings offer insights into the behavior of quantum fields in curved spacetime and contribute to the understanding of the information paradox in quantum gravity.

This project explores the black hole information paradox by integrating quantum computing, holographic entanglement entropy, and black hole thermodynamics. I employed variational quantum eigensolvers (VQEs) to model black hole dynamics and compute holographic entanglement entropy (HEE) using Schwarzschild-AdS and Reissner-Nordström-AdS metrics. By simulating Hawking radiation and black hole evaporation, I analyzed the entropy evolution and assess information preservation. The project addresses key challenges such as metric singularities, numerical integration issues, and entropy calculation discrepancies. These findings offer insights into the behavior of quantum fields in curved spacetime and contribute to the understanding of the information paradox in quantum gravity.

2024 - 2024

Quantum Topological Hybrid Error Correction

Don T DiscloseComputer Code ProgrammingPolygonPoint Key Point
This project conceals a hybrid algorithm that integrates quantum simulations with classical machine learning for error correction in topological quantum codes. The approach leverages an innovative method: it utilises the mathematical structure of Lie algebras and topological anyons, combined with dynamic error correction strategies and machine learning techniques.

This project conceals a hybrid algorithm that integrates quantum simulations with classical machine learning for error correction in topological quantum codes. The approach leverages an innovative method: it utilises the mathematical structure of Lie algebras and topological anyons, combined with dynamic error correction strategies and machine learning techniques.

2024 - 2024

Quantum AI Protein Sequence Folding Prediction and Model Comparison

Internal Proprietary ToolingMedical DicomPolygonEntity Ner Classification
This project fetches protein sequences from UniProt, preprocesses the data, and trains both a classical deep learning model and a hybrid model combining quantum computing. It evaluates and compares model performance in terms of accuracy, training time, and memory usage, with visualizations for accuracy and loss.

This project fetches protein sequences from UniProt, preprocesses the data, and trains both a classical deep learning model and a hybrid model combining quantum computing. It evaluates and compares model performance in terms of accuracy, training time, and memory usage, with visualizations for accuracy and loss.

2024 - 2024

Particle Filter for Nonlinear Systems

Internal Proprietary Tooling3D SensorPolygon
This project contains a Python implementation of a Particle Filter for Nonlinear Systems (PFNN). The PFNN class is designed to estimate the state of a nonlinear system using a particle filter approach. This method is particularly useful for systems where the state dynamics are complex and cannot be easily modeled.

This project contains a Python implementation of a Particle Filter for Nonlinear Systems (PFNN). The PFNN class is designed to estimate the state of a nonlinear system using a particle filter approach. This method is particularly useful for systems where the state dynamics are complex and cannot be easily modeled.

2024 - 2024

Education

U

Università Degli Studi Di Palermo

Bachelor's Degree, Computer Science

Bachelor's Degree
2024 - 2024

Work History

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Francesco D. hasn’t added any Work History to their OpenTrain profile yet.