Doctoral School

Reading date: 27/03/2025

• MÜLLER RIGAT, GUILLEM-JACOB: Certifying quantum resources in many-body systems from accessible observables
  
  Author: MÜLLER RIGAT, GUILLEM-JACOB
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 18/02/2025
  Reading date: 27/03/2025
  Reading time: 09:00
  Reading place: Elements room and online https://teams.microsoft.com/l/meetup-join/19%3ameeting_OWRjZmVmNzctMDA5MC00OGJiLWFiMjYtNDEyMWI0NzhkYjY5
  Thesis director:
  Thesis abstract: This thesis primarily aims at developing reliable theoretical tools to certify the preparation of entangled states and other quantum correlations in many-body systems from accessible observables. In doing so, we reconcile various information-theoretic measures to the laboratory byconstructing witnesses that can be readily tested in current experiments. In the course of this work, we address the certification of a number of resources related to quantum entanglement ranging from coherence to Bell nonlocality. A common aspect among these resources is their convexity, namely, the fact that the resource content cannot be produced nor amplified by mere statistical mixing of different states. This observation is also a key technical property for almost all of our contributions. Here, we focus on those many-body systems that are most easily probed by permutation-invariant or collective observables, such as spin ensembles or spinor Bose Einstein condensates. In this respect, the symmetries of the observables can be leveraged to construct entanglement criteria with a more favorable scaling. The resource content of a physical system is certified from the statisticsit produces. Within the quantum formalism, such statistics are encoded in the density matrix, which is reconstructed based on finite information from experimentally available probes. We start the thesis by outlining a practical machine-learning assisted protocol to improve and denoise the inference of such statistics in realistic scenarios. Subsequently, we discuss the certification of metrologically useful entanglement by introducing a simple algorithm to evaluate the minimal quantum Fisher information compatible with a set of arbitrary mean values. Our approach enables to systematically tighten well-known spin-squeezing parameters and reveal the sensing power of many-body states with minimal experimental effort. Next, we address the detection of entanglement from averages and uncertainties of collective observables by formulating a single condition testing a number of witnesses, including those proposed in the past such as the generalized spin squeezing inequalities. We apply our approach to unveil new entanglement witnesses tailored to Bose-Einstein condensates based on Zeeman sublevels populations. We also discuss, to some extent, the witnessing of the Schmidt number, the central bipartite entanglement measure, using similar observables. Then, we tackle the converse problem of detecting separable states from mathematical techniques based on invertible positive maps. The last part of the thesis is devoted to Bell nonlocality, one of the strongest forms of nonclassicality beyond quantum entanglement. We first scale Bell dimension witness, i.e. criteria whose violation signals the impossibility of explaining the inferred statistics with a Hilbert space of a given local dimension, to the many-body regime. In particular, we propose that the violation depth of a specific three-outcome Bell inequality can be used to robustly certify the number of qutrits in an ensemble. We close the thesis by presenting a data-driven approach to detect Bellnonlocality from one- and two-body spin correlations averaged over all permutation of parties. This methodology allows us to discover tighter Bell inequalities tailored to spin squeezed states and many-body spin singlets of arbitrary spin.
  

Reading date: 28/03/2025

• VILAR ALGUERÓ, RICARD: QUANTUM ANALOGS OF CLASSICAL CODES
  
  Author: VILAR ALGUERÓ, RICARD
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN APPLIED MATHEMATICS
  Department: School of Mathematics and Statistics (FME)
  Mode: Normal
  Deposit date: 07/03/2025
  Reading date: 28/03/2025
  Reading time: 09:30
  Reading place: Sala de Juntes de la FME, Edifici U, Campus Sud
  Thesis director: BALL MARKS, SIMEON MICHAEL
  Thesis abstract: The main focus of this thesis are stabilizer codes, a type of error-correcting code used to correct quantum information that has been corrupted by noise. We introduce several new general constructions of stabilizer codes. In particular we use one of the constructions to construct quantum cyclic redundancy check codes, an error-correcting code which is used in classical information to correct burst errors. We show how to use a quantum version of such codes to correct burst errors on systems of quantum bits. We include a geometric description of stabilizer codes, extending previous constructions which work only for the qubit case to quantum systems in which the quantum particles have local dimension p, where p is any prime number. Finally, we reduce the problem of ascertaining when a generalised Reed-Solomon code is contained in its Hermitian dual and therefore can be used to construct a stabilizer code. This reduction allows us to determine the shortest and longest length of generalised Reed-Solomon codes which are contained in their Hermitian dual, verifying a conjecture of Grassl and Rotteler.
  

Reading date: 31/03/2025

• HERNÁNDEZ CHULDE, CARLOS EFRÉN: Software defined networking for autonomous and secure optical networks
  
  Author: HERNÁNDEZ CHULDE, CARLOS EFRÉN
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
  Department: Department of Signal Theory and Communications (TSC)
  Mode: Normal
  Deposit date: 04/03/2025
  Reading date: 31/03/2025
  Reading time: pending
  Reading place: pending
  Thesis director: CASELLAS REGI, RAMON | MARTINEZ RIVERA, RICARDO VICTOR
  Thesis abstract: The increasing complexity and demands of modern telecommunications networks necessitate the development of autonomous and secure systems to ensure efficient, reliable, and secure communications. The integration of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) together with Quantum Key Distribution (QKD) into optical networks addresses these needs. This integration enables the creation of networks that can efficiently automate their operations while ensuring the highest standards of security. In this context, this thesis explores the use of Software Defined Networking (SDN) for the advancement of autonomous and secure optical networks, in particular Elastic Optical Networks (EONs). The research focuses on enhancing network efficiency and security to meet the growing complexity and demands for high-capacity, low-latency, and secure communications.The PhD thesis investigates the application of ML, specifically Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to tackle key challenges in the management and optimization of EONs. The primary goal is to develop autonomous and intelligent solutions for dynamic service provisioning, resource allocation, and spectrum management. A significant contribution of this work is the development of novel DRL-based approaches for Routing and Spectrum Assignment (RSA). These methods are designed to adaptively manage network resources in real-time, overcoming the limitations of traditional, static RSA algorithms. By considering latency as a key factor, the DRL-based RSA mechanism ensures the efficient provisioning of latency-sensitive applications and improves overall network performance metrics, such as latency and throughput. The thesis also examines the dynamic provisioning and optimal placement of Virtual Network Functions (VNFs) using DRL and GNNs. This combination of technologies enables a more efficient mapping of resource requirements to the physical infrastructure, facilitating scalable and flexible network management systems.The research also includes an experimental validation of the proposed solutions. A proof-of-concept (PoC) was implemented to demonstrate the integration of DRL models within an SDN control plane framework. This involved externalizing path computation to a dedicated entity that assists the SDN controller in the path and spectrum selection function. The experimental results confirmed the practical applicability of the DRL approach in supporting selected control functions in operational EON infrastructures.Furthermore, the research explores the coexistence of Continuous Variable Quantum Key Distribution (CV-QKD) and classical channels within EONs, which is essential for ensuring secure communications in the quantum computing era. To address the challenge of noise interference from high-power classical channels on sensitive quantum channels, the thesis introduces dynamic spectrum allocation strategies leveraging SDN. These strategies optimize the use of spectrum resources and minimize noise interference, ensuring secure and efficient operation of the integrated network.In summary, this thesis provides significant advancements in the field of autonomous and secure optical networks by integrating advanced ML techniques, contributing to the development of agile, high-capacity, reliable, and secure EONs for future telecommunications.
  

Reading date: 02/04/2025

• PÉREZ I GONZALO, RAÜL: End-to-end learning for wind turbine blades: from imagery data to defect repair recommendations
  
  Author: PÉREZ I GONZALO, RAÜL
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION
  Department: Institute of Robotics and Industrial Informatics (IRI)
  Mode: Normal
  Deposit date: 26/02/2025
  Reading date: 02/04/2025
  Reading time: 11:00
  Reading place: Sala d'Actes de la Facultat de Matemàtiques i Estadística (FME) de la Universitat Politècnica de Catalunya, C/de Pau Gargallo, 14, 08028 Barcelona
  Thesis director: AGUDO MARTÍNEZ, ANTONIO
  Thesis abstract: The European Union's (EU) reliance on external energy sources underscores the urgent need for energy security and affordability, driving the transition to renewable energy with wind power as a key renewable solution. However, wind turbine operation and maintenance still account for 30% of energy production costs, due to their prolonged exposure to harsh environmental conditions. Timely defect detection and repair are critical, as turbines must often be halted during visual inspections and repairs. Streamlining the process from inspection to decision-making is essential to reduce downtime and operational costs.This thesis presents a comprehensive end-to-end blade assessment system designed to determine defect severity, quantify their impact on energy production, and deliver actionable repair recommendations. By enabling wind turbine owners to act proactively, this system helps minimize operational costs. The framework emphasizes efficient image transmission that preserves quality, followed by the generation of detailed blade assessments to establish a consistent and effective repair strategy.To this end, this project proposes first segmenting images to isolate blade regions, simplifying subsequent tasks through algorithms tailored for imagery acquired under diverse conditions. These include a Blade U-Net model, which introduces dense conditional-random-field regularization to enhance segmentation accuracy, and advanced post-processing involving iterative refinement through hole-filling and noise reduction via an unsupervised random forest. Two deep discriminant analysis frameworks integrate class separability and probabilistic modeling into robust non-linear architectures to derive precise defect boundaries, handle complex textures, and improve generalization across varied inspection data. Additional contributions include a modular region-growing classifier for efficient segmentation in data-scarce conditions and diffusion-based models with dual-space augmentation to improve generalization and robustness, leading to substantial superior performance than competing techniques. Together, these segmentation methods form the foundation for automated defect detection and diagnostics.In the second part, to address the challenge of handling large volumes of high-resolution inspection data, this work also presents a novel region-of-interest (ROI) image compression framework. Traditional methods often compromise critical defect information. The proposed framework leverages segmentation outputs to ensure high-fidelity compression in blade regions, employing lossless or high-quality lossy techniques while aggressively compressing non-relevant areas. Key innovations include multi-layer nested latent variable models for lossy coding and parallelized bits-back coding optimized for industrial-scale applications. These advancements achieve state-of-the-art performance while significantly reducing computational costs. By coupling compression with our proposed multi-task defect detection model, this approach supports timely and accurate diagnostics, ensuring minimal disruption to turbine operations.In summary, this thesis contributes a hierarchy of low-level to high-level algorithms designed to streamline wind turbine maintenance processes. The combination of advanced segmentation and compression enables a fully automated pipeline for blade defect assessment, encompassing defect localization, classification, and repair prioritization, directly improving energy efficiency by reducing downtime, optimizing maintenance schedules, and minimizing repair costs.
  

Reading date: 04/04/2025

• UGRINOVIC KEHDY, NICOLAS: Modeling and Reconstruction of 3D Humans under Context
  
  Author: UGRINOVIC KEHDY, NICOLAS
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ARTIFICIAL INTELLIGENCE
  Department: Department of Computer Science (CS)
  Mode: Normal
  Deposit date: 05/02/2025
  Reading date: 04/04/2025
  Reading time: 15:00
  Reading place: Sala de Juntes de la FME
  Thesis director: SANFELIU CORTES, ALBERTO | MORENO NOGUER, FRANCESC D'ASSIS
  Thesis abstract: The study of human's and their behavior through the analysis of images and videos has long been a central topic in Computer Vision. The reconstruction and modeling of human behavior have garnered increasing attention, due to their potential applications in virtual environments, including AR/VR, sports, fashion, and the film industry. Despite this growing interest, accurately capturing and generating the 3D pose and motion of humans remains an important challenge, primarily due to the vast diversity of human movements and the inherent complexity of the human body. Furthermore, the ability to capture and replicate subtle human interactions---such as a hug---that are intuitively understood by humans continues to be a significant obstacle for machines. This complexity arises from the need for a deep understanding of the physical world, its constraints, and the nuanced ways in which humans interact with it.This thesis presents the development of several methodologies for reconstructing and modeling various aspects of humans in 3D, including detailed shape, pose, and motion, mainly from RGB images. A key emphasis is placed on capturing or incorporating contextual information as additional information. First, we introduce a method for modeling the detailed body shape of individuals, which includes elements such as clothing across a wide range of poses. Subsequently, the focus shifts to the simultaneous pose estimation of multiple individuals, wherein scene constraints are employed to enhance the accuracy of these estimations. This approach addresses the fundamental challenges of depth and scale ambiguity inherent in 3D reconstruction. The work is then extended into the temporal domain, to reconstruct interacting individuals, particularly in scenarios involving close interactions. A significant challenge under such situations is the lack of compliance with physical laws, such as body collisions. To address this, we integrate a fully-featured physics simulator within a motion estimation pipeline to account for these physical inconsistencies. Lastly, we propose a method capable of generating human motion that interacts with a virtual environment. All proposed methods have undergone extensive evaluation.In summary, this thesis introduces a suite of tools for the modeling and reconstruction of 3D humans, advancing the field towards more accurate capture and recreation of realistic behavior for virtual humans, with a particular emphasis on their interactions with its surrounding environment.