08 Fakultät Mathematik und Physik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/9

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Open Access
Optimizing NV magnetometry for magnetoneurography and magnetomyography applications
(2023) Zhang, Chen; Zhang, Jixing; Widmann, Matthias; Benke, Magnus; Kübler, Michael; Dasari, Durga; Klotz, Thomas; Gizzi, Leonardo; Röhrle, Oliver; Brenner, Philipp; Wrachtrup, Jörg
Magnetometers based on color centers in diamond are setting new frontiers for sensing capabilities due to their combined extraordinary performances in sensitivity, bandwidth, dynamic range, and spatial resolution, with stable operability in a wide range of conditions ranging from room to low temperatures. This has allowed for its wide range of applications, from biology and chemical studies to industrial applications. Among the many, sensing of bio-magnetic fields from muscular and neurophysiology has been one of the most attractive applications for NV magnetometry due to its compact and proximal sensing capability. Although SQUID magnetometers and optically pumped magnetometers (OPM) have made huge progress in Magnetomyography (MMG) and Magnetoneurography (MNG), exploring the same with NV magnetometry is scant at best. Given the room temperature operability and gradiometric applications of the NV magnetometer, it could be highly sensitive in the pT/Hz-range even without magnetic shielding, bringing it close to industrial applications. The presented work here elaborates on the performance metrics of these magnetometers to the state-of-the-art techniques by analyzing the sensitivity, dynamic range, and bandwidth, and discusses the potential benefits of using NV magnetometers for MMG and MNG applications.
Open Access
Quantum kernel methods and applications to differential equations
(2024) Flórez Ablan, Roberto
Quantum computers have the potential to surpass classical computers in specific tasks, promising advantages in many fields. Machine Learning (ML), a domain with significant societal impact, is a key area of interest for exploring the applications of quantum computing. Here, we investigate two research directions aimed at understanding how current quantum computers can be used to solve ML problems. First, we study Quantum Kernels (QKs). By calculating inner products between quantum states, QKs can be used to define similarity measures between points. QKs are a promising approach to Quantum Machine Learning (QML) but, in general, they have not been shown to outperform classical ML methods. A key reason for this is that QKs suffer from the exponential concentration problem. As the number of qubits increases, the kernel matrices become similar to the identity matrix, preventing generalization. One strategy to alleviate the exponential concentration problem is to rescale the data points that enter the quantum model. This technique is known as bandwidth tuning and has been shown to allow generalization in QKs. However, it has been numerically demonstrated that using this method results in QKs that cannot provide a quantum advantage over classical methods. In this thesis, we propose an explanation for this phenomenon. We show that due to the size of the rescaling factors, the QKs become similar to polynomial and RBF kernels, which are classically tractable. Second, we implemented a Differential Equation (DE) solver based on variational quantum methods. A Quantum Neural Network (QNN) or QK, is used to represent an ansatz for the solution of a DE. The DE information is included into a loss function, which is minimized using a classical optimizer. In the case of a QK, the optimized parameters are the coefficients of a linear combination of QKs evaluated at the data points. In the case of a QNN, the optimized parameters are the phases of the quantum gates. The QNN implementation was included into the open-source QML python library sQUlearn. A preliminary hyperparameter study was conducted for QKs. Based on our limited investigation, we conclude that QKs leveraging the fidelity between quantum states, known as Fidelity Quantum Kernels (FQKs), demonstrate superior performance compared to those employing a semi-classical approach, referred to as Projected Quantum Kernels (PQKs).
Open Access
Smart nesting : estimating geometrical compatibility in the nesting problem using graph neural networks
(2023) Abdou, Kirolos; Mohammed, Osama; Eskandar, George; Ibrahim, Amgad; Matt, Paul-Amaury; Huber, Marco F.
Reducing material waste and computation time are primary objectives in cutting and packing problems (C &P). A solution to the C &P problem consists of many steps, including the grouping of items to be nested and the arrangement of the grouped items on a large object. Current algorithms use meta-heuristics to solve the arrangement problem directly without explicitly addressing the grouping problem. In this paper, we propose a new pipeline for the nesting problem that starts with grouping the items to be nested and then arranging them on large objects. To this end, we introduce and motivate a new concept, namely the Geometrical Compatibility Index (GCI). Items with higher GCI should be clustered together. Since no labels exist for GCIs, we propose to model GCIs as bidirectional weighted edges of a graph that we call geometrical relationship graph (GRG). We propose a novel reinforcement-learning-based framework, which consists of two graph neural networks trained in an actor-critic-like fashion to learn GCIs. Then, to group the items into clusters, we model the GRG as a capacitated vehicle routing problem graph and solve it using meta-heuristics. Experiments conducted on a private dataset with regularly and irregularly shaped items show that the proposed algorithm can achieve a significant reduction in computation time (30% to 48%) compared to an open-source nesting software while attaining similar trim loss on regular items and a threefold improvement in trim loss on irregular items.