Abstracts Archive

Classical dynamic simulations of proteins in solution are the state of the art method for providing an atomic-level view of the protein dynamics, structure, and thereby function. Still the simulation data include hidden information, which can be extracted using Machine Learning algorithms. We have p...

Quantum kernel methods offer compelling possibilities for enhanced machine learning [1,2]; however, the extent to which the specific noise characteristics of near-term quantum computers impact their performance remains an open question. To accurately assess the feasibility of near-term quantum machi...

The design of a transport aircraft requires a large number of load computations for an optimal structural sizing. Different flight points, maneuver and gust load cases, as well as different mass and failure cases, and control laws need to be taken into account, easily summing up to hundred...

Physics-informed machine learning is increasingly used in applied scientific computing. Physics-informed neural networks (PINNs) [Raissi19] embed governing physical laws directly into the training objective or model structure, thus enabling the solution of partial differential equations (PDEs) witho...

(pdf file attached Abstract.pdf  for linked sources :)1 Problem description and relevanceNext to efficiently optimizing a circuit covering an exponentially growing Hilbert space, quantum machinelearning (QML) faces the difficult task of mapping classical data to the quantum computer such that u...

Identifying exoplanets from light curves has become a primary method for exoplanet detection, with many deep learning classification models having been applied to exoplanetary transit data. Due to the difficulty of detecting exoplanets, mission data is heavily biased towards non-exoplanet light...

State-of-the-art algorithms in quantum mechanics, computational science and engineering are widely known and established. In this regard, recent advancements in quantum computing promise an improvement by offering a speedup over classical computing methods [1]. To this end, the efficient implementat...

Quantum kernel methods promise classification advantages in high-dimensional feature spaces by computing kernel functions in exponentially large Hilbert spaces, yet a critical practical bottleneck remains unresolved: how to prepare classical input features for quantum circuits with limited qubit cou...

Quantum circuits are inherently parametric and optimization of the parameters is required. While this might be explicit as with a Variational-Quantum-Algorithm (VQA), or implicit as with the Quantum-Singular-Value-Transform, the optimal choice of parameters depends not only on the desired algorithm,...