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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

Quantum annealing

In this video we review the basics of a paradigm in quantum computing called quantum annealing. Different from the gate-based model we are often exposed to, quantum annealing became the first approach to compete with classical algorithms in certain tasks, mostly related to optimization. We also give a few examples where quantum annealing can be applied in the scope of optimizing cost functions. 

Prerequisite knowledge

  • Basic knowledge about Hamiltonians and spin systems. 
  • Basic knowledge about optimization methods. 

Main takeaways

  • Gate-based models are not the only paradigm employed in quantum computing. Quantum annealing has been around for a while. 
  • Further progress in quantum computing will be with gate-based models because of scalability issues with quantum annealing. 
  • Quantum annealing serves as a good understanding of how physical systems can be mapped to optimization schemes in industrial problems. 

Further thinking

Which field is better suited for quantum annealing?

a. Driving routes
b. Combinatory
c. Protein folding
d. All of the above

Solution

Further reading

Quantum annealing in the transverse Ising model. Tadashi Kadowaki and Hidetoshi Nishimori Phys. Rev. E 58, 5355–5363 (1998)

A numerical implementation of quantum annealing Diego de Falco et al. Conference: Stochastic Processes, Physics and Geometry July, 1988

Quantum Annealing for Industry Applications: Introduction and Review S. Yarkoni Et al. arXiv: 2112.07491