Tootfinder

Countering Privacy Nihilism
Severin Engelmann, Helen Nissenbaum
https://arxiv.org/abs/2507.18253 https://arxiv.org/pdf/2507.18253

Countering Privacy Nihilism
Of growing concern in privacy scholarship is artificial intelligence (AI), as a powerful producer of inferences. Taken to its limits, AI may be presumed capable of inferring "everything from everything," thereby making untenable any normative scheme, including privacy theory and privacy regulation, which rests on protecting privacy based on categories of data - sensitive versus non-sensitive, private versus public. Discarding data categories as a normative anchoring in privacy and data protecti…

Quantum-inspired algorithm for simulating viral response
D. O. Konina, D. I. Korbashov, I. V. Kovalchuk, A. A. Nizamieva, D. A. Chermoshentsev, A. K. Fedorov
https://arxiv.org/abs/2506.15671

Quantum-inspired algorithm for simulating viral response
Understanding the properties of biological systems is an exciting avenue for applying advanced approaches to solving corresponding computational tasks. A specific class of problems that arises in the resolution of biological challenges is optimization. In this work, we present the results of a proof-of-concept study that applies a quantum-inspired optimization algorithm to simulate a viral response. We formulate an Ising-type model to describe the patterns of gene activity in host responses. Re…

Prediction of Protein Three-dimensional Structures via a Hardware-Executable Quantum Computing Framework
Yuqi Zhang, Yuxin Yang, William Martin, Kingsten Lin, Zixu Wang, Cheng-Chang Lu, Weiwen Jiang, Ruth Nussinov, Joseph Loscalzo, Qiang Guan, Feixiong Cheng
https://arxiv.org/abs/2506.22677

Prediction of Protein Three-dimensional Structures via a Hardware-Executable Quantum Computing Framework
Accurate prediction of protein active site structures remains a central challenge in structural biology, particularly for short and flexible peptide fragments where conventional methods often fail. Here, we present a quantum computing framework specifically developed for utility-level quantum processors to address this problem. Starting from an amino acid sequence, we formulate the structure prediction task as a ground-state energy minimization problem using the Variational Quantum Eigensolver …