福利姬

Responsible: Jan-脜ke Larsson

Research in quantum foundations and computing investigates the principles that make quantum information processing fundamentally different from classical information processing. The work combines quantum information theory, foundations of quantum mechanics, and quantum computing, with a particular focus on the resources that enable quantum advantage, the structure and implementation of quantum algorithms, and the role of contextuality, nonlocality, and Bell inequalities in quantum theory. A central theme is to understand both the power and the limitations of quantum technologies: from efficient implementations of quantum computational primitives to rigorous analysis of loopholes, measurement assumptions, and security issues in quantum cryptography.

Responsible: Guilherme B. Xavier

Research in quantum technologies is centered on experimental quantum communication and photonic quantum information processing. The activities are closely connected to the Quantum Technologies Laboratory at Link枚ping University, founded in 2018, where much of the division鈥檚 recent experimental work has been carried out. The research focuses on quantum key distribution, distribution and certification of entanglement, high-dimensional photonic states, quantum random number generation, integrated and all-fiber photonic devices, and quantum communication over conventional and next-generation optical-fiber infrastructures. A recurring goal is to bring quantum communication closer to practical telecommunication environments, including work on multicore and few-mode fibers, coexistence with classical data traffic, time-bin and energy-time entanglement, semi-device-independent certification and fiber-integrated quantum network components such as switches and buffers.

Responsible: Onur G眉nl眉

Research in information theory develops mathematical foundations and coding-theoretic methods for secure, private, and efficient information processing. Current activities include distributed computation, privacy for machine learning, information-theoretic security, biometric and hardware-intrinsic security, and secure semantic compression. The work connects classical information-theoretic limits with emerging applications such as secure 6G integrated sensing and communication, coded distributed computing, privacy-utility trade-offs, and future communication and computation systems. A key objective is to design methods whose security and privacy guarantees can be quantified from first principles, supporting long-term trustworthy communication and computation even as implementation platforms evolve.

Responsible: Shizhen Chang; senior researchers: Lena Klas茅n and Niclas Fock

Research in digital forensics develops AI- and data-driven methods for analysing, validating, and interpreting digital evidence. The activities include deep learning for image forgery detection and analysis, detection of manipulated or AI-generated media, explainable and robust forensic models, forensic noise-pattern analysis, and methods for handling large-scale multimodal evidence. The area also connects to image analysis, computer vision, remote sensing, and applied AI for criminal investigations. Through Lena Klas茅n and Niclas Fock, the work is strongly connected to Digital Forensics Sweden and to national collaboration with law enforcement, public authorities, industry, and AI Sweden, with the broader aim of strengthening Sweden鈥檚 capacity to investigate cybercrime, digital manipulation, and technology-enabled crime.

Responsible: Arunava Naha

Research in secure and resilient cyber-physical systems focuses on the security, reliability, and control of networked systems in which sensing, computation, communication, and actuation are tightly integrated. The work addresses how control systems can detect and respond to adversarial manipulation, including deception and replay attacks, using methods such as quickest detection, sequential change detection, and physical watermarking. It also studies risk-aware and learning-based control, including reinforcement-learning and policy-gradient methods for optimal control under probabilistic and chance constraints. The overall aim is to develop control strategies that remain trustworthy and efficient under uncertainty, limited information, and malicious interference.