Germanium Quantum Technologies Development
This course is part of Quantum 301: Semiconductor QC Technology.
Explore cutting-edge semiconductor quantum computing technology focusing on germanium qubits. Study their physics, advantages, and challenges compared to other platforms. Learn about fabrication processes, control mechanisms, and practical applications including quantum error correction and algorithms. Gain insights from industry professionals about the latest developments in the field.
Instructors:
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What you'll learn
Understand physics and advantages of germanium qubits
Master qubit control and machine learning automation
Explore semiconductor industry facilities and capabilities
Implement quantum algorithms with germanium qubits
Apply quantum error correction techniques
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 delves into the development and applications of germanium quantum technologies. Students explore semiconductor device physics, material growth techniques, and quantum dot control systems. The curriculum covers quantum error correction, algorithm implementation, and machine learning applications for auto-tuning. Special focus is placed on industrial approaches, CMOS technology integration, and practical challenges in quantum computing development.
Semiconductor Devices and Materials
Module 1
Germanium Qubits
Module 2
Tuning Quantum Dots
Module 3
Quantum Error Correction and Algorithms
Module 4
Final Assessment
Module 5
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Quantum 301: Semiconductor QC Technology
Instructors
8 Courses
Pioneering Leader in Semiconductor Quantum Computing
Menno Veldhorst serves as an associate professor at QuTech, Delft University of Technology, where he has established himself as a pioneer in semiconductor quantum computing. His groundbreaking achievements include the first-ever demonstration of single and two-qubit gates in silicon during his postdoctoral research at the University of New South Wales, which was recognized as one of the top-ten breakthroughs in physics in 2015. Leading his research group at QuTech, he has achieved numerous significant firsts in the field, including the demonstration of a planar germanium quantum dot, a single-hole qubit, two-qubit logic in germanium, a four-qubit quantum processor, and a 16 quantum dot system operated with shared control. His innovative work continues to push the boundaries of quantum computing technology, particularly in the development of semiconductor-based quantum systems.
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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