Quantum Computing Applications
Permanent URI for this collectionhttps://hdl.handle.net/10125/107579
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Item type: Item , Quantum Feature Embeddings for Graph Neural Networks(2024-01-03) Xu, Sascha; Wilhelm-Mauch, Frank; Maass, WolfgangQuantum computing offers a promising avenue to reduce growing machine learning model complexity as required in large language models and simulation models for weather forecasts, financial forecasts, or engineering. Graph neural networks are a particular class of machine learning models that have garnered much attention for their ability to deal well with structured data. We investigate how to enhance existing GNNs and find through the inductive bias that quantum circuits are used best to encode node features. The proposed Quantum Feature Embeddings (QFEs) turn raw input features into quantum states, enabling non-linear and entangled representations. In particular, QFEs provide normalized, non-redundant weight matrices in an exponentially larger feature space and require much fewer qubits than fully quantum graph neural networks. On standard graph benchmark datasets, we showcase that for the same parameter count QFEs perform better than their classical counterpart, and are able to match the performance of an exponentially larger model. Finally, we study the potential benefit of using a hybrid quantum graph neural network over a classic alternative on a concrete use case, laser cutting. We find that the proposed model has the performance and thus the near-term potential to uplift these business applications.Item type: Item , Graph-controlled Permutation Mixers in QAOA for the Flexible Job-Shop Problem(2024-01-03) Palackal, Lilly; Richter, Leonhard; Hess, MaximilianOne of the most promising attempts towards solving optimization problems with quantum computers in the noisy intermediate scale era of quantum computing are variational quantum algorithms. The Quantum Alternating Operator Ansatz provides an algorithmic framework for constrained, combinatorial optimization problems. As opposed to the better known standard QAOA protocol, the constraints of the optimization problem are built into the mixing layers of the ansatz circuit, thereby limiting the search to the much smaller Hilbert space of feasible solutions. In this work we develop mixing operators for a wide range of scheduling problems including the flexible job shop problem. These mixing operators are based on a special control scheme defined by a constraint graph model. After describing an explicit construction of those mixing operators, they are proven to be feasibility preserving, as well as exploring the feasible subspace.Item type: Item , Towards Requirements Engineering for Quantum Computing Applications in Manufacturing(2024-01-03) Stein, Hannah; Schroeder, Stefan; Kienast, Pascal; Kulig, MarcoQuantum computing (QC) shows the potential to trigger a paradigm shift for numerous industries. As an emerging technology, methodological support for designing and developing QC-based applications is lacking. This paper presents the results of a case study applying consortium research in order to perform a requirements engineering process for two QC-based applications in the manufacturing industry. The results show the differences between requirements engineering for QC applications and conventional software applications. The major findings point to the need for QC knowledge and best practices for a successful requirements engineering process and elaborate on the main differences between QC application- and software application requirements.Item type: Item , Introduction to the Minitrack on Quantum Computing Applications(2024-01-03) Wilhelm-Mauch, Frank; Struckmeier, Frederick; Maass, Wolfgang