b4l4m(He/Him)

I am a quantum computing researcher and developer from Mexico working at the intersection of quantum information, optimization, machine learning, scientific computing, and data science.

I lead Qubit.mx, an initiative focused on quantum computing research, education, technical collaboration, and community building in Mexico. Through Qubit.mx, I work on developing learning and research activities that connect students, developers, researchers, and the broader international quantum ecosystem.

My current technical interests include quantum optimization, QAOA, QUBO and Ising formulations, quantum machine learning, hybrid quantum-classical algorithms, quantum error correction, post-quantum cryptography, benchmarking, noise analysis, and the practical evaluation of quantum algorithms on simulators and quantum hardware.

I develop and experiment primarily with PennyLane, Qiskit, Python, and cloud quantum computing environments. I am particularly interested in understanding when quantum approaches can provide meaningful computational advantages and how their performance changes with problem size, circuit depth, noise, precision requirements, and computational resources.

One of my main research interests is combinatorial optimization. I have worked on mapping assignment and optimization problems into QUBO/Ising formulations and studying their implementation using variational quantum algorithms such as QAOA and hybrid quantum-classical workflows.

My background also includes experimental physics and scientific data analysis at the Instituto de Física, UNAM, where I worked with experimental data related to Feshbach resonances, Bose–Einstein condensates and possible quantum turbulence phenomena. This experience strengthened my interest in connecting computational methods with fundamental physical systems.

Beyond quantum computing, I have professional experience in data science, data governance, machine learning, software engineering, cloud technologies, ETL/ELT architectures, and large-scale data systems. This background influences how I approach quantum computing: with an emphasis on reproducibility, benchmarking, scalability, and realistic comparisons between classical and quantum methods.

I am especially interested in collaborating on research involving quantum optimization, QML, variational algorithms, benchmarking, quantum simulation, and the study of simulability, accuracy, computational resources, and scaling of quantum algorithms.

I am also interested in contributing to open-source quantum software and collaborating with researchers, developers, universities, and quantum technology organizations internationally.