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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 Machine Learning

In this video we review the basics of quantum machine learning. Firmly believed to be one of the areas in quantum computing providing some quantum advantage the earliest, quantum machine learning incorporates quantum phenomena in order to boost classical algorithms in machine learning. From supervised methods to unsupervised ones, quantum machine learning is showing promising features to deal with large amounts of data and improve training times in specific tasks. 

Prerequisite knowledge

  • Basic knowledge about machine learning. 
  • Basic knowledge about quantum computing and quantum circuits. 

Main takeaways

  • Quantum machine learning has the potential to show advantage in the NISQ era. 
  • There are already promising works in the field showing interesting features to deal with data, particularly to handle quantum data. 
  • Very applicable to many fields of interest for several domains, not only useful for physicists. 

Further thinking

What is the worst caveat of using QAOA?

a. Bad entanglement structure
b. Complexity of the circuit
c. Hard classical optimization
d. None of the above

Solution

Further reading

Quantum Machine Learning J. Biamonte et al. Nature 549, 195-202 (2017)

Variational QuUantum algorithms M. Cerezo et al. Nature Review Physics 3, 625-644 (2021)

An introduction to QML M. Schuld et al. Contemporary Physics 56,2, 172-185