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Germanium Quantum Technology

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 /Auto-tuning a quantum computer 30 /Tuning and operation of arrays 31 /Finding operation points example 32 /What is auto-tuning? 33 /Neural network tuning 34 /Navigating charge stability diagrams 35 /Experimental implementation 36 /Towards universal quantum algorithms 37 /Classical error correction 38 /Quantum error correction 39 /Progress and challenges 40 /Introduction to quantum algorithms 41 /The first algorithms 42 /Quantum annealing 43 /Quantum Machine Learning

Towards universal quantum algorithms

In this video we present outlook and current state of art of auto-tuning algorithms.

Main takeaways

  • Ultimately, we are looking for algorithms that are scalable, universal and not device-specific 
  • There is wide range of successful tuning algorithms that were experimentally demonstrated 
  • Challenges for the field looking forward include in-situ control, standardization of training data, development of new algorithms that can tackle the scaling challenges. 

Further thinking

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?

a. Reinforcement learning.
b. Swarm optimization.
c. Generative models.
d. Deep convolutional networks.

Solution

Further reading

Contemporary experimental references mentioned in the lecture:
https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.17.024069 
https://www.nature.com/articles/s41534-021-00434-x 
https://www.nature.com/articles/s41534-019-0193-4