Speakers

Claudia Zendeja-Morales
University of Copenhagen, Department of Mathematical Sciences, Centre for the Mathematics of Quantum Theory (QMATH) – Copenhagen, Denmark

Claudia Zendejas-Morales es física e ingeniera en computación. Su trabajo y participación comunitaria han sido reconocidos internacionalmente al ser seleccionada en el Quantum 100 del International Year of Quantum Science and Technology (IYQ 2025), liderado por la UNESCO. Ha cursado dos MicroMasters en Tecnologías Cuánticas por Purdue University y es desarrolladora certificada de Qiskit y Qiskit Advocate. Participa activamente en QWorld, donde ha colaborado como instructora y asistente de docencia, desarrolló el módulo de QML para los QCourses, es co-coordinadora del área de QEducation y miembro de QWorld Association. Actualmente es estudiante de doctorado en Quantum Information Science en la Universidad de Copenhague. Sus intereses se centran en información cuántica teórica, machine learning cuántico, fundamentos matemáticos de la computación cuántica y juegos no locales cuánticos.

TALK:
Community organizations in quantum technologies

Quantum computing communities are often associated with outreach and education, but they can also become spaces for developing technical projects, gaining research experience, and building collaborations. This talk presents examples from communities like QWorld, QuantumQuipu, and the Qiskit Advocates network: from coordinating and adapting materials on algorithms and quantum machine learning to mentoring projects, creating tutorials, and studying strategies to mitigate exponential concentration in quantum kernels. Experiences in hackathons will also be discussed, where distributed teams transformed ideas into prototypes of games, hybrid models, and quantum software. Based on these cases, the discussion will explore how community work allows for delving deeper into specialized content, assuming technical and scientific responsibilities, and developing communication, leadership, and collaboration skills. These experiences can complement academic training and pave the way for postgraduate studies and research.

Dr. Cristian Barría Huidobro
Director, Cybersecurity Research Center – Universidad Mayor, Santiago, Chile

PhD in Computer Engineering, with postdoctoral studies in multicultural education and social media management. Master of Science in Computer Engineering and Master of Science in Educational Planning and Management. Bachelor of Science in Computer Science and Engineering, Computer Engineer, and Management Engineer. Director of the Cybersecurity Research Center at Universidad Mayor, Chile. Author of books and publications in computer science. In 2025, he received the Outstanding Professor Award from the IEOM – U.S. Society International for his academic contributions in the field of cybersecurity.

TALK:
Bits, Qubits, Quantum Computing

Quantum computing is one of the most promising technologies and, at the same time, one of the most surrounded by expectations and myths. This lecture, based on the book Bits, Qubits, Quantum Computing, offers a critical yet accessible look at its scientific foundations, the current state of its development, and the challenges it still faces. Key questions will be addressed, such as: How close are we to quantum supremacy? What impact will it have on security, artificial intelligence, and industry? And how much of what is being announced corresponds to real progress and how much to speculation? The goal is to provide a rigorous and balanced perspective that allows for an understanding of the true potential of this technology and its implications for science, engineering, and society.

Dr. Cristian E. Susa Quintero
Full Professor – Department of Physics and Electronics, University of Córdoba, Montería, Colombia.

Cristian Edwin Susa Quintero is a Physicist and holds a PhD in Physics (2016, University of Valle, Colombia), specializing in quantum correlations and open quantum systems. He currently teaches Physics at the University of Córdoba (Montería, Colombia), where he leads the Theoretical and Applied Physics Group and advises undergraduate and graduate students in physics, contributing to the academic and scientific development of the region and the country.

His research focuses on quantum information theory, with lines of work in quantum correlations, entanglement theory, and quantum optics. In recent years, he has expanded his research to include quantum control, physics-informed neural networks (PINNs), and other machine learning schemes, exploring their application to problems in quantum physics.

He has published over 20 articles in specialized international journals and has participated in numerous international conferences, several of them as an invited speaker, sharing his research results with the global scientific community.

TALK:
Physically Informed Neural Networks Applied to the Optimal Control of Quantum Gates

In this talk, we present a framework based on physically informed neural networks (PINNs) that predicts control functions for implementing one- and two-qubit quantum gates. Our approach incorporates the Schrödinger equation and the Lindblad master equation directly into the training loss function, along with terms that guarantee gate fidelity through the target task and constraints on the density matrix, thus extending the method recently introduced by Norambuena et al. [1] for state-transfer control. For the single-qubit Hadamard gate, the trained network achieves an average fidelity of 99.99998% (tested on a sample of 1000 random initial states). Extending the scheme to the two-qubit CX controlled negation gate, we performed an analysis by coupling type (σx⊗σx, σy⊗σy, and σz⊗σz interactions), obtaining average infidelities (1 – fidelity) on the order of 10⁻⁶ for transverse and longitudinal couplings, and 10⁻⁴ for σy⊗σy, showing that the gate’s performance depends on the coupling structure between qubits.

We conclude by briefly describing other related lines of research that we are currently developing: hybrid classical-quantum PINNs to solve the one-dimensional Schrödinger equation, symbolic regression for the discovery of effective theories, and reinforcement learning for the optimization of molecular control.

Dr. Pío José Arias González
Postgraduate Coordinator – Facultad de Ciencias (UCV), Venezuela

Samuel Felipe Serna Otalvaro
Associate Professor, Bridgswater State University & MIT