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Patrik Sund

Publications and source records attributed to Patrik Sund.

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Experimental Quantum Key Distribution in an Indefinite Causal Order

In quantum physics the order in which different operations occur can be placed in superposition. The resulting processes have an indefinite causal order and are both of fundamental interest and can be viewed as a novel quantum resource that enables a variety of new protocols. Here we report an experimental implementation of one such protocol, where we perform BB84-like quantum cryptography by placing Alice and Bob's measurement-and-preparation operations in a photonic quantum SWITCH. By embedding Alice and Bob within the quantum SWITCH, the protocol achieves an average eavesdropper detection probability of $0.15 \pm 0.02$ per shared qubit, with eavesdropper detection performed through measurements of the control qubit rather than by comparing the key. Unlike the standard BB84 and related schemes, which detect eavesdropping by publicly revealing and discarding a fraction of the raw key, our approach requires no disclosure of key material: every retained qubit can, in principle, be tested for eavesdropping while remaining available for key generation. The experiment relies on a new measurement technique that allows the polarization of a photon to be measured inside the quantum SWITCH without destroying path coherence. Although the present implementation does not yet constitute a secure quantum key distribution protocol, owing to the post-selection required for measurements within the quantum SWITCH, it provides a proof of principle that indefinite causal order can be exploited to detect eavesdropping without sacrificing key bits.

quant-ph

Medical Image Understanding Improves Survival Prediction via Visual Instruction Tuning

Accurate prognostication and risk estimation are essential for guiding clinical decision-making and optimizing patient management. While radiologist-assessed features from CT scans provide valuable indicators of disease severity and outcomes, interpreting such images requires expert knowledge, and translating rich visual information into textual summaries inevitably leads to information loss. In this work, we propose a vision-language framework for 3D CT image understanding that leverages large-scale open-sourced CT images paired with radiology reports through visual instruction tuning. This pre-training enables the model to learn clinically meaningful visual-textual representations, which can then be adapted to downstream survival prediction tasks. By incorporating a survival prediction head on top of the pre-trained model, our approach improves survival prediction from CT images and clinical data while generating clinically meaningful language responses to predefined questions. Experimental results demonstrate that our method outperforms baseline methods in survival prediction, particularly, when clinical data alone is less predictive. The code will be released upon acceptance.

cs.CV