ML for Semiconductor Quantum Devices
Learn to apply machine learning techniques for controlling and calibrating semiconductor quantum computing chips in this advanced course.
Learn to apply machine learning techniques for controlling and calibrating semiconductor quantum computing chips in this advanced course.
This comprehensive course focuses on applying machine learning techniques to semiconductor quantum computing devices. Students will learn to develop and implement machine learning solutions for various quantum device tuning tasks, from coarse configuration adjustment to fine-tuning and unsupervised data analysis. The course combines theoretical understanding with practical Python implementation, preparing students to integrate AI-driven solutions into quantum research and engineering workflows. Designed for advanced learners with relevant master's-level background, it bridges the gap between quantum computing hardware control and modern machine learning approaches.
Instructors:
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What you'll learn
Understand how machine learning can be applied to semiconductor quantum device tuning
Formulate quantum device tuning challenges as machine learning problems
Develop Python-based machine learning prototypes for quantum computing tasks
Implement supervised and unsupervised learning techniques for quantum dot analysis
Assess machine learning suitability for quantum computing workflows
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This advanced course combines quantum computing and machine learning, focusing on controlling semiconductor quantum devices. Students learn to implement machine learning solutions for quantum dot tuning, from basic configuration to fine-tuning. The curriculum covers supervised and unsupervised learning techniques, practical Python implementation, and real-world applications in quantum computing workflows. The course emphasizes hands-on experience with PyTorch and includes both theoretical foundations and practical demonstrations.
Supervised Learning for Quantum Dot Configuration Tuning
Module 1
Charge Tuning with Neural Networks
Module 2
Unsupervised Learning for Quantum Dot Data Analysis
Module 3
Fine-tuning with Neural Networks
Module 4
Conclusion and Recap
Module 5
Fee Structure
Instructor
4 Courses
Quantum Computing and AI Innovation Leader
Eliška Greplová serves as an associate professor at the Kavli Institute of Nanoscience and leads the Quantum Matter and AI group at Delft University of Technology, where she pioneers research at the intersection of quantum computing, artificial intelligence, and condensed matter physics. After completing her PhD at Aarhus University under Klaus Mølmer's supervision and postdoctoral research at ETH Zurich in Sebastian Huber's group, she established herself as a leading figure in quantum technology innovation. Her research portfolio includes groundbreaking work in developing algorithmic methods for quantum computing experiments, applying AI to understand complex condensed matter physics phenomena, and implementing emergent condensed matter phenomena in quantum devices. As a member of the World Economic Forum's Council on Future of Quantum Economy and former Visiting Researcher at Microsoft AI4Science, she brings a unique perspective to quantum technology development. Her group has secured significant funding, including a 5 million EUR Kavli Institute Innovation Award and NWO Quantum Delta funding, while making substantial contributions to quantum education through initiatives like the EdX course on Machine Learning for Semiconductor Quantum Devices. Her most influential work includes developing unsupervised identification methods for topological phase transitions, adversarial Hamiltonian learning techniques for quantum dots, and new approaches for quantifying non-stabilizerness in quantum circuits, establishing her as a pioneering force in bridging quantum computing with artificial intelligence.
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