When:
Spring 2025     starting date Wed Feb 19
Lectures:          Wednesdays, 8:15 am (CET) - 10 am
Exercises:         tbd
Official course book listing

Where:                   
Online course, everyone please sign up here
Basel students: please also sign up through the system @unibas as always.

Teachers:          
Dr. Henry Legg (St. Andrews), Dr Ji Zou (Basel), Dr Denis Kurlov (Basel), Dr Peter Stano (RIKEN) & experimental colleagues from the NCCR SPIN network

Coordinators: Prof. Jelena Klinovaja, Prof. Daniel Loss, Prof. Dominik Zumbühl

Email:     
spin.qubit.basel@gmail.com

Download Flyer

Grades and Credits (KP):          
will be awarded based on the problem sets, which are collected and graded.

Quantum mechanics was first developed about a hundred years ago and has since become one of the great overarching theories. Remarkably, quantum-mechanical principles such as superposition and entanglement enable novel types of computer, quantum computers, capable of solving otherwise intractable problems.

In this course, we will discuss how semiconductor spins can be used for quantum information processing. We will review basic operations of one and two qubits, keeping an eye on how to implement them in semiconducting devices. We will study spin qubits in quantum dots [1], and focus on standard industry materials silicon [2] and germanium [3], currently among the most promising platforms for a large-scale quantum computer.

Starting from the basic concept of a qubit, ideas such as entanglement, prospects for scaling and integration, noise (using the Lindblad master equation), k.p theory for semiconductors, and state-of-the-art research topics, including recent experimental progress, will be discussed.

This course is tailored to master and PhD students, with theoretical (or experimental) background, aiming to widen their perspectives into the fast-growing field of spin-based quantum information processing.

This year, for the first time, lectures will be recorded and made available on an online platform, allowing participants to interact with the recordings and pose course-related questions to an AI-powered teaching assistant for an in-class-like experience. The background materials have been carefully tailored by experts to ensure the relevance and quality of the answers provided. Please note that the AI teaching assistant is currently in the beta testing phase, and any feedback is highly appreciated.

[1] D. Loss and D. P. DiVincenzo, Phys. Rev. A 57, 120 (1998).
[2] L. Vandersypen and M. Eriksson, Phys. Today 72, 38 (2019).
[3] G. Scappucci, C. Kloeffel, et al., Nature Reviews Materials 6, 926 (2021).