{"id":700,"date":"2025-11-12T14:26:56","date_gmt":"2025-11-12T12:26:56","guid":{"rendered":"https:\/\/webs.uab.cat\/phynest\/?p=700"},"modified":"2025-11-26T12:26:07","modified_gmt":"2025-11-26T10:26:07","slug":"exploring-physical-properties-of-semiconducting-qubits-with-artificial-intelligence-based-computational-workflows","status":"publish","type":"post","link":"https:\/\/webs.uab.cat\/phynest\/2025\/11\/12\/exploring-physical-properties-of-semiconducting-qubits-with-artificial-intelligence-based-computational-workflows\/","title":{"rendered":"Exploring physical properties of Semiconducting qubits with Artificial Intelligence-based computational workflows"},"content":{"rendered":"\n
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\"Research<\/figure>\n\n\n\n

The PhD project is fully aligned with the cutting-edge research at ICN2 on the modelling of semiconducting spin qubits within the rapidly evolving field of quantum computing technologies. It is embedded in a vibrant international network, with Roche\u2019s group maintaining close collaborations with leading experimental teams fabricating qubits at the Niels Bohr Institute (Denmark), IST Austria, QuTech\/TU Delft (Netherlands), and with the ICN2 experimental group of J. Arbiol, which applies state-of-the-art STEM techniques for structural characterization.<\/p>\n\n\n\n

The thesis pursues an ambitious technical objective: the development of a fully functional computational workflow that integrates (i) DFT-based materials databases, (ii) machine learning\u2013derived interatomic potentials (notably MACE potentials), and (iii) automated Hamiltonian generation. These outputs will feed into the group\u2019s in-house large-scale quantum transport code (LSQUANT, www.lsquant.org), enabling the exploration of local electronic and spin properties in digital qubit models reconstructed directly from experimental images and data. <\/p>\n\n\n\n

Beyond methodological advances, the project will provide a quantitative assessment of key qubit figures of merit, by exploiting the newly developed tight-binding models together with complementary codes accounting for electrostatic environments and screening effects. The expected outcome is a unique multiscale framework that bridges atomic-scale imaging, first-principles modelling, and device-level simulation, thereby delivering novel insights into the design principles governing scalable spin-based quantum technologies.<\/p>\n\n\n\n

\"Academic<\/figure>\n\n\n\n

Master in quantum science and technology, experience in PYTHON coding, solid background in condensed Matter physics and computational science (machine learning techniques,\u2026)<\/p>\n\n\n\n

\"Research<\/figure>\n\n\n\n

ICREA Research Professor Stephan Roche leads the Theoretical & Computational Nanoscience Group at the Catalan Institute of Nanoscience and Nanotechnology (ICN2, Barcelona), internationally recognized for advancing the theory and simulation of quantum materials and devices. The group\u2019s expertise covers quantum charge and thermal transport, spin dynamics, and device modeling, with a strong focus on topological matter, graphene and 2D materials, and van der Waals heterostructures. Pioneers in linear-scaling quantum transport methodologies, the group has enabled unrivalled large-scale simulations of disordered and complex systems, bridging the gap between atomic-scale modeling and experimental reality. These methods are now coupled with Artificial Intelligence and machine learning tools, providing ab-initio accuracy in trillion-atom scale models and accelerating the discovery of novel functionalities in nanomaterials. Their work underpins key advances in quantum technologies, spintronics, nanoelectronics, and thermoelectrics, positioning the group as a global leader in predictive multiscale simulation for both fundamental science and future applications.<\/p>\n<\/div>\n\n\n\n

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THESIS SUPERVISORS<\/h6>\n\n\n\n