Quantum Computing

Quantum computing not only brings great opportunities, but also new risks for cyber security. As quantum computers could be able to solve highly complex problems exponentially faster than conventional computers, there will be risks to confidential data in the future. Attackers could use a quantum computer to break through encryption or carry out new types of cyberattacks that overwhelm traditional security systems. At the same time, quantum algorithms can contribute to improving cyber security, for example through more complex random number generators, new encryption techniques or the identification of security problems.

Fraunhofer AISEC is researching the secure use of quantum computers and the use of quantum computing to increase cyber security.

quantum computer with neural network visualization

Quantum Neural Network Security: Kill-Chain Attacks on QNNs

Quantum machine learning is moving from theory to real quantum hardware. This creates new security risks. In a recent study, researchers from Fraunhofer AISEC and partners demonstrated a multi-stage attack on a quantum neural network running on trapped-ion hardware. The attack combines side-channel analysis, adversarial examples and crosstalk. For providers, product teams and decision-makers, the message is clear: quantum AI needs security by design before it becomes a critical infrastructure.

Pascal Debus 29. September 2026

Towards Classical Software Verification using Quantum Computers

In this post, we explore the possibility of accelerating the formal verification of classical programs using quantum computers. Common programming errors, such as null-pointer dereference and out-of-bound access, are prevalent sources of security flaws. Our approach involves generating a Satisfiability (SAT) instance from code snippets, which is satisfiable if the undesired behavior exists. This instance is then converted into an optimization problem, solved using quantum algorithms, thus potentially achieving asymptotically polynomial speedup.

Sebastian Issel 13. August 2025

Quantum and Classical AI Security: How to Build Robust Models Against Adversarial Attacks

The rise of quantum machine learning (QML) brings exciting advancements such as higher levels of efficiency or the potential to solve problems intractable for classical computers. Yet how secure are quantum-based AI systems against adversarial attacks compared to classical AI? A study conducted by Fraunhofer AISEC explores this question by analyzing and comparing the robustness of quantum and classical machine learning models under attack. Our findings about adversarial vulnerabilities and robustness in machine learning models form the basis for practical methods to defend against these attacks, which are introduced in this article.

Maximilian Wendlinger 13. December 2024

Anomaly Detection with Quantum Machine Learning – Identifying Cybersecurity Issues in Datasets

Since the release of ChatGPT, the popularity of Machine Learning (ML) has grown immensely. Besides Natural Language Processing (NLP) anomaly detection is an important branch of data analysis whose goal is to identify observations or events that deviate from the rest of the data. At Fraunhofer AISEC, cybersecurity experts explore Quantum Machine Learning methods for anomaly detection. One approach is based on the classification of quantum matter while a second method uses a type of Quantum Support Vector Machine with a kernel that is calculated on a quantum computer. This blog post explains the fundamentals of anomaly detection and shows the two approaches being pursued by the Quantum Security Technologies group at Fraunhofer AISEC.

Kilian Tscharke 30. January 2024