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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

Experimental implementation

What is the most common reason for neural network misclassification that was experimentally observed?

d. Missed transition that yields under-counting of the electron number.

It is the most often the case that the neural network misses a transition due to noise. When that happens the total number of electrons in the dot gets under-counted.