The CEC Quantum Computing is aimed at professionals who:

• Want to integrate quantum computing into data science applications
• Strive for future viability in data science, artificial intelligence (AI), or technical research
• Want to use quantum algorithms for optimization or machine learning problems
• Want to acquire the basics for working in the quantum field

Upon completion of the course, graduates will have:

• a sound understanding of quantum information theory
• practical skills for implementing quantum algorithms
• in-depth knowledge of quantum machine learning (QML)
• the ability to evaluate the potential of quantum computing for real-world data problems

Quantum Information Theory

Contents

• Qubits and quantum registers
• Superposition and entanglement
• Quantum gates

Learning objectives

• Understand the basic principles of quantum mechanics
• Analyze quantum protocols
• Apply quantum cryptography

Quantum Algorithms and Programming

Contents

• Algorithm design
• Error correction
• Practical experience with Qiskit/PennyLane

Learning objectives

• Implement core algorithms (e.g., Grover, Shor)
• Solve optimization problems with quantum methods
• Develop quantum code in Python

Applied Quantum Computing in Data Analysis

Contents

• Optimization applications
• Data analysis integration
• Case studies

Learning objectives

• Develop quantum solutions for clustering/classification
• Integrate quantum systems into classical architectures
• Evaluate practical relevance

Quantum machine learning

Content

• Variational quantum circuits
• Hybrid approaches
• Application examples

Learning objectives

• Design hybrid QML models
• Apply quantum kernel methods
• Compare QML with classical ML

The course includes face-to-face classes with interactive quantum simulations, hands-on labs with Qiskit and PennyLane, analysis of real-world case studies from industry and research, group projects with access to quantum hardware (IBM Quantum), and guided self-study in a cloud-based lab environment.
The course is offered in a hybrid format: the instructor is always present on site, and students participate in on-site classes or join online.

Lessons are held on Wednesday evenings from 5:00 p.m. to 8:30 p.m. Participants will receive timetable one month before the course begins. The CEC holidays are based on the school holidays in the city of Zurich.

Dates 2026: 25.02 / 04.03 / 11.03 / 18.03 / 25.03 / 01.04 / 08.04 / 15.04 / 06.05 / 13.05

The team of lecturers consists of proven experts with academic and practical expertise. Here is an excerpt from the list of lecturers:

• Dr. Pavel Sulimov: Senior Quantum AI Researcher, Institute of Computer Science, ZHAW School of Engineering; Academic Lead, Quantum Algorithms Expert Group at Innosuisse AI Booster.
• Prof. Dr. Rudolf Marcel Füchslin: Head of the Group for Applied Complex Systems Sciences, ZHAW School of Engineering; Co-Director of the European Centre for Living Technology at Venice, Italy
• Claude Lehmann: Research Associate, Institute of Computer Science, ZHAW School of Engineering
   

Collaboration with IBM Quantum and QuantumBasel

The existing collaboration with IonQ and QuantumBasel gives participants direct access to state-of-the-art quantum computers via cloud platforms. This collaboration results in practical and industry-relevant case studies that promote the transfer of theoretical knowledge into real-world applications. In addition, students benefit from intensive knowledge transfer through guest lectures by renowned quantum experts, who provide insights into current research, technological developments, and industrial applications.
 

Admission to the course generally requires a university degree (university of applied sciences, HTL, HWV, university, ETH). However, practitioners with comparable professional competence may also be admitted if their eligibility to participate is demonstrated by other evidence.

Recommended prior knowledge:

• Sound programming skills in Python
• Basic understanding of linear algebra and statistics (short refresher course at the beginning of the first teaching unit)
• Basic knowledge in the field of machine learning

We do neither keep waiting lists nor place reservations.

Should a place become available in the previous CEC, we will consider the order in which registrations were received.