Description

Student Assistant (Wissenschaftliche Hilfskraft) / Bachelorthesis / Masterthesis

Quantum Algorithms for Applications in IT Security

Motivation

Quantum computing is a technology with the potential to bring about disruptive changes in many industries and enable a wide range of new applications. Our research goal is to lay the foundations for a secure and trustworthy use of this new technology from the very beginning and to develop solutions to make quantum computing productively usable for IT security. We want to investigate how quantum algorithms for optimization or quantum machine learning (QML) can be applied to various tasks in IT Security like software verification or anomaly detection, what potential quantum advantages can be identified and what is scope and time scale of these advantages.

Task Description

For student assistants (and depending on the scope, also thesis students), typical tasks usually involve initial literature research and summary, implementation and evaluation of an algorithm and preparation of the results for publication, project status or (industry) partner meetings. Based on our currently running project within the Munich Quantum Valley, different aspects of the work may include, but are not limited to:

• Adversarial QML (e.g., evaluation of attacks against QML Algorithms, defense mechanism and robust formulations)
• Anomaly and Fraud Detection (e.g., application and evaluation of QML or ‘quantum inspired’ classical algorithms (tensor networks) to security-relevant use-cases, impact of data encoding)
• Software Testing (e.g., quantum-assisted verification methods for SAT, SMT, Bounded Model Checking problems related to software verification)

Please, state your topics of interest in your application. Prospective thesis students are also welcome to suggest own topic proposals at the intersection of IT Security, machine learning and quantum computing.

Requirements

• Good programming skills in Python
• Basic knowledge of quantum computing and machine learning
• Familiarity with machine learning libraries (e.g., PyTorch) and Quantum SDKs (e.g., Qiskit, Pennylane) is a plus
• Ability to work self-directed and systematically
• Motivation and self-organization

Contact

Fraunhofer Research Institution for Applied and Integrated Security (AISEC)

Lichtenbergstraße 11, 85748 Garching (near Munich), Germany

https://www.aisec.fraunhofer.de