Quantum 301: Quantum Computing with Semiconductor Technology

The semiconductor qubit platform attracts significant investment because it is compatible with the CMOS manufacturing processes that built the modern computer industry. Germanium hole-spin qubits have shown rapid progress since 2018, demonstrating fast two-qubit gates and the potential for higher operating temperatures than competing platforms.

This two-course programme from QuTech at Delft goes deeper into semiconductor quantum technology than any other online programme. A collaboration between several institutions with state-of-the-art facilities for fabrication, control, and application of Germanium qubits.

What you’ll learn

• The physics of hole-spin qubits in Germanium: why Germanium’s band structure gives it advantageous spin-orbit coupling for fast qubit control
• Quantum dot formation in Germanium: gate-defined quantum dots, their energy level structure, and how quantum information is encoded in spin states
• Pauli spin blockade: the measurement mechanism that enables single-shot spin readout
• Fabrication processes for Germanium qubit devices: materials deposition, electron beam lithography, etching, and the cleanroom workflow
• The semiconductor industry landscape: companies, facilities, and supply chains relevant to quantum computing hardware
• Electrical control components: cryogenic amplifiers, arbitrary waveform generators, and the room-temperature electronics that operate at millikelvin temperatures
• The auto-tuning problem: why manual qubit calibration does not scale and why machine learning is needed
• Machine learning for charge stability diagrams: classifying quantum dot operating regimes automatically
• Bayesian optimisation for efficient gate voltage search
• Reinforcement learning approaches to adaptive qubit control
• Quantum error correction strategies relevant to semiconductor qubit arrays
• How quantum algorithms run on semiconductor hardware today

Course structure

Quantum 301 consists of two courses taken sequentially.

Course 1 - Development and Applications of Germanium Quantum Technologies Covers the physics and fabrication side. Opens with Germanium qubit physics: band structure, hole spin properties, and what makes Germanium advantageous. Fabrication follows: device geometry, cleanroom processes, and the industry ecosystem. Qubit control and readout come next: how gate voltages tune quantum dot levels, how exchange coupling creates two-qubit gates. The course closes with quantum error correction and algorithm execution on Germanium hardware.

Course 2 - Machine Learning for Semiconductor Quantum Devices Covers ML-assisted control and calibration. Opens with the auto-tuning problem: the exponential complexity of calibrating many qubits manually. Classification of charge stability diagrams follows, using supervised machine learning. Bayesian optimisation for gate voltage search and reinforcement learning for adaptive control complete the ML content. Integration into a hardware control stack closes the course.

Both courses run at six to seven hours per week.

Who is this for?

• Semiconductor engineers, materials scientists, and device physicists entering quantum hardware development
• Machine learning engineers and data scientists applying their skills to quantum device calibration
• Researchers in experimental quantum computing seeking formal coverage of semiconductor qubits
• Anyone who has completed Quantum 101 and wants to specialise in hardware

Prerequisites

Completion of Fundamentals of Quantum Information and The Hardware of a Quantum Computer (both from Quantum 101) is strongly recommended. Background in semiconductor physics - quantum wells, band structure, field effect transistors - is helpful. Python programming is required for ML course exercises. This is a genuinely advanced programme requiring substantial prior preparation.

Hands-on practice

The two courses provide complementary hands-on work:

Course 1 exercises: analysing charge stability diagrams from real device data, evaluating fabrication sequences, and calculating expected qubit performance from device parameters.

Course 2 exercises: Python programming assignments training classifiers on stability diagram images, implementing Bayesian optimisation for gate voltage search, and building a reinforcement learning agent for qubit control. Uses open-source ML libraries on realistic quantum device datasets.

Why take this course?

Semiconductor spin qubits are widely viewed as one of the most scalable paths to fault-tolerant quantum computing. Companies including Intel, QuTech (via Quantum Inspire), Imec, and several startups are actively developing this platform.

This programme is taught by the QuTech group producing world-leading results in Germanium qubits. The combination of device physics, fabrication knowledge, and ML-assisted control in a single programme is unique in quantum computing education. For anyone seriously targeting quantum hardware research or development, there is no better structured online preparation.