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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

Classical error correction

Now that you have learned how spin-qubits in Germanium are created, tuned, and operated, you will learn another critical aspect of quantum computers: quantum error correction. Quantum error correction is crucial for performing error-free quantum algorithms and scaling quantum computers beyond the few tens of qubits achievable today.

This video introduces a few basic concepts of error correction in classical information theory as a preparation for quantum error correction, which is presented in Parts 2 and 3. These include the repetition code, the concepts of the decoder, the majority vote decoder and the maximum likelihood decoder, performance measures of error correcting codes, code distance, break-even point, and error threshold. 

Prerequisite knowledge

  • Elementary probability theory

Main takeaways

  • Information encoded in physical bits is corrupted due to noise imposed on the bits by their environment. 

  • Noise effects can be mitigated if the information is stored redundantly. 

  • Increasing the redundancy can lead to increased protection. 

  • For the classical repetition code, the simplest decoder is the majority vote decoder. 

  • Often-used concepts to characterize error correction schemes are the logical error probability, the break-even point, and the error threshold.

Further thinking

True or False: For a typical error correction scheme, if the physical error is below the break-even point, then the logical error is below the physical error.

Solution

Further reading

This classic textbook has a chapter on quantum error correction, which starts with a with a description of the classical repetition code.
Michael Nielsen and Isaac Chuang, Quantum Information and Quantum Computation, Cambridge University Press. Chapter 10.1
https://www.cambridge.org/highereducation/books/quantum-computation-and-quantum-information/01E10196D0A682A6AEFFEA52D53BE9AE#overview