Close

Filter courses

Show courses Show filters
Close

Germanium Quantum Technology Solutions

01 /What can CMOS technology do for quantum computing? 02 /Semiconductor devices and scaling with industrial approach 03 /Semiconductor foundry facilities: the good and the bad for qubits 04 /IMEC's latest qubit developments 05 /Summary and outlook 06 /Semiconductors for spin qubits 07 /Germanium quantum wells on silicon 08 /Germanium quantum wells on silicon-germanium 09 /Growth methods 10 /Characterization techniques 11 /Electrical characterization of germanium quantum wells 12 /Fabrication of H-FET 13 /Semiconductor qubits: vision and challenges 14 /Quantum materials 15 /Quantum dot qubits and their operation 16 /Types of quantum dot qubits 17 /Qubit Readout 18 /Qubit Control 19 /Qubit Coherence and Fidelity 20 /Multi-qubit control and quantum algorithms 21 /Quantum links and scaling 22 /Physics of holes 23 /Hole spin qubits 24 /Introduction to electronics for quantum computing 25 /Room temperature electronics 26 /Cryogenic qubit chip carriers 27 /Contribution to IGNITE 28 /Quantum Control Stack for Spin Qubits 29 /What is auto-tuning? 30 /Neural network tuning 31 /Navigating charge stability diagrams 32 /Experimental implementation 33 /Towards universal quantum algorithms 34 /Classical error correction 35 /Quantum error correction 36 /Progress and challenges 37 /Introduction to quantum algorithms 38 /The first algorithms 39 /Quantum annealing 40 /Quantum Machine Learning

Towards universal quantum algorithms

We discussed several types of machine learning algorithms that are currently included in contemporary quantum dot experiments. Which one of the following is not an example of such algorithm?

b. Swarm optimization.

We did not discuss global optimization methods in this lecture at all, including the swarm optimization. While in principle it could be applied to tuning, at the time of creating this course there are no known experimental examples of its implementation.