Category Quantum Computing

From Early Warning Signs to the Workbench: the PQC Update 2026 Shows that the Post-Quantum Era Has Begun

As we kicked off the PQC Update 2026, one question hung in the air: Is post-quantum cryptography still a distant dream – or has it long since become part of everyday life for government agencies, industry, and standards bodies? The answers from our speakers were surprisingly concrete: Dutch guidelines, German ID cards with PQC, new security chips, updated internet standards, roadmaps for critical infrastructure, and tools that can already reveal your legacy cryptographic vulnerabilities today. If you just want to know whether you need to take action now: Yes. If you want to know how, read on.

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.

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.

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.