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Dit algoritme lost wachtrijen in ouderenzorg op: plaatsing tot 4 keer sneller - TW - 15-05-2025
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Criminelen verzamelen nu al data voor de quantumcomputer van morgen - ICT/Magazine - 24-06-2025
No full textNieuw lectoraat Datagedreven publieke veiligheid geeft impuls aan publieke veiligheid - Bedrijfsgoed - 03-03-2025
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The versatility of evolutionary intelligent tri-objective treatment planning for cervical cancer brachytherapy
Full text linkBackground: A multi-objective automated treatment planning approach, called BRIGHT, has demonstrated success in prostate cancer brachytherapy (BT). BRIGHT optimizes directly on dose-volume metrics, aligning with clinical protocol goals, and produces multiple plans that represent different trade-offs between tumor coverage and healthy organ sparing. Current automated treatment planning methods either do not optimize directly on dose-volume metrics or generate a single plan, which is only considered optimal in the specific optimization model. Purpose: We extended BRIGHT to cervical cancer BT, for which adding a third objective to the existing bi-objective approach was deemed necessary. In this work, we present the algorithmic adaptations made to the approach and highlight its flexibility, which enables straightforward inclusion of customizations. We further demonstrate that this approach produces clinically acceptable plans. Methods: The first two objectives in the proposed approach pertain to the EMBRACE-II protocol, which is divided into tumor coverage and healthy organ sparing. The third objective encompasses added aims, which were deemed necessary to be included to ensure dose distribution shape characteristics not captured in the EMBRACE-II protocol but which can also readily be tuned to include local clinical preferences. We illustrate this by proposing four different customizations: a baseline customization and three different customizations that lead to (potentially distinct) pear-shaped dose distributions, often desired in cervical cancer BT. We include optimization with contiguous volumes, a capability distinctive to BRIGHT, as an option for dose distribution shape optimization. We tested all four customizations on 269 BT fractions (123 patients), and studied differences in runtimes, 3D dose distributions, as well as obtained dose-volume values. Clinical acceptability was evaluated for six representative patient cases, by presenting the resulting set of plans for all customizations to a BT team of two radiation oncologists, a medical physicist, and a radiation therapy technologist. They were asked to assess whether there is at least one acceptable plan per patient in the given set of plans. Results: Treatment plans can be generated in under 2.8 min with the baseline tri-objective BRIGHT, or 3.7 min if contiguous volumes are included, even though 260.000 dose calculation points are used for highly accurate dose estimation during optimization. There are visual differences in dose distributions for some of the six patient cases when using the distinct customizations, although generally pear-shaped distributions were obtained. The contiguity of the dose distributions resulting from optimizing with contiguous volumes can be advantageous in special cases where the high-dose region is preferred in the target area, as well as directly being tied to the location of the inserted applicator. Achieved dose-volume values are clinically comparable between all four customizations. The BT team indicated that 3/4 customizations included at least one clinically acceptable plan for all six patients. Conclusions: Clinically acceptable plans for cervical cancer BT can be quickly generated using the new tri-objective version of BRIGHT. This approach allows for straightforward customization to accommodate local clinical preferences. We demonstrated this versatility through various customizations that produced generally pear-shaped, yet potentially distinct, dose distributions, with comparable dose-volume values according to the EMBRACE-II protocol
On quantum position verification : security and experimental constraints
Full text linkThis doctoral thesis explores quantum position verification (QPV)---a cryptographic task where one attempts to confirm someone’s geographical location. The core question that QPV aims to answer is: Are you truly at the location you claim to be? To achieve this, QPV combines two pillars of the fundamental laws of nature: (i) special relativity, which limits the speed at which information can travel to the speed of light in vacuum, and (ii) quantum mechanics, which governs the behavior of quantum particles in ways that defy intuition. QPV protocols rely on (i) timing communication between the entities involved in the protocol, and (ii) transmitting information encoded in quantum particles.
However, quantum hackers may attempt to pretend to be at the claimed location while actually being elsewhere. This raises the critical question: Can one be certain that the claimed location is genuine and not forged by hackers?
This thesis presents new advances toward a fundamental understanding of QPV, its security against powerful quantum hackers, and the feasibility of secure QPV protocols despite experimental challenges that, if exploited by hackers, can severely compromise their security. Furthermore, related to attacks on QPV protocols, this thesis analyzes quantum correlations that emerge in broader cryptographic primitives
Space-efficient quantum error reduction without log factors
Full text linkGiven an algorithm that outputs the correct answer with bounded error, say , it is sometimes desirable to reduce this error to some arbitrarily small -- e.g., if one wants to call the algorithm many times as a subroutine. The usual method, for both quantum and randomized algorithms, is majority voting, which incurs a multiplicative overhead of from calling the algorithm this many times.
Transducers are a recently introduced model of quantum computation, and it is possible to reduce the "error" of a transducer arbitrarily with only constant overhead, using a construction analogous to majority voting called purification. Even error-free transducers map to bounded-error quantum algorithms, so this does not let you reduce algorithmic error for free, but it does allow bounded-error quantum algorithms to be composed without incurring log factors.
In this paper, we present a new highly simplified purifier, that can be understood as a weighted walk on a line similar to a random walk interpretation of majority voting. Our purifier has much smaller space and time complexity than the previous one. Indeed, it only uses one additional counter, and only performs two increment and two decrement operations on each iteration. It also has quadratically better dependence on the soundness-completeness gap of the algorithm being purified. We prove that our purifier has optimal query complexity, even down to the constant, which matters when one composes quantum algorithms to super-constant depth.
Purifiers can be seen as a way of turning a "Monte Carlo" quantum algorithm into a "Las Vegas" quantum algorithm -- a process for which there is no classical analogue. Our simplified construction sheds light on this strange quantum phenomenon, and could have implications for the complexity of composed quantum algorithms
How well do LLMs reason over tabular data, really?
No full textThis repository contains the benchmark suite and replication package for our paper "How well do LLMs reason over tabular data, really?", presented at the 4th Table Representation Learning Workshop at ACL 2025. It lets you reproduce the reasoning tests from the paper and explore how different models perform on table reasoning challenges