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quant-ph2026
How Hard Is Quantum Advantage? A Cloud Microphysics Stress Test for Variational Quantum Models
Felix Herbort, Ellen Sarauer, Daniel Ohl de Mello +7
Quantum machine learning (QML) could have the potential to leverage advantages of quantum over classical computing but still lacks strong evidence of actual improvements and scalab…
quant-ph2026
Operator Learning for efficient Quantum Computation
Paul Over, Sergio Bengoechea, Leonardo Borello Busilacchi +3
An efficient implementation of quantum algorithms is often hindered by the lack of efficient primitives for operators and state preparation. This limits both the ability of near-te…
quant-ph2025
Quantum Bayesian Optimization for the Automatic Tuning of Lorenz-96 as a Surrogate Climate Model
Paul J. Christiansen, Daniel Ohl de Mello, Cedric Brügmann +6
In this work, we propose a hybrid quantum-inspired heuristic for automatically tuning the Lorenz-96 model -- a simple proxy to describe atmospheric dynamics, yet exhibiting chaotic…