Student-Abstracts-EN

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Quantum-Multi-Agent-Reinforcement-Learning mit Evolutionärer Optimierung (en)

Title

Abstract:

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Author:

Max Mustermann

Advisors:

Claudia Linnhoff-Popien

Student Thesis | Published {Month YYYY} | Copyright © QAR-Lab
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Anomaly Detection using Quantum Circuit Born Machines (en)

Anomaly Detection using Quantum Circuit Born Machines

Abstract:

Anomaly detection is a critical component in various fields, including finance, medical diagnosis, and fraud detection. As datasets become increasingly complex and larger, traditional computers face limitations in processing power. In contrast, quantum computers oer promising solutions through the physical properties of their qubits, such as entanglement and superposition. The emergence of quantum machine learning, particularly the quantum circuit born machines (QCBMs), is introduced as a promising approach to tackle such complex problems. QCBMs are parameterized quantum circuits that can be trained to generate samples from a target distribution. The goal of this work is to leverage this ability for detecting anomalies that have a distribution dierent from that of normal data points. The effectiveness of QCBMs for anomaly detection is explored using a dataset generated by the make_blobs method from the Scikit-learn package in Python, where some outliers can be clearly distinguished from the clusters. And its performance is compared with an autoencoder model using the ROC-curve and the Matthews correlation coecient (MCC). These metrics are used to evaluate the models’ ability to detect anomalies and avoid false positives. The results show that QCBMs outperform the autoencoder when trained with a smaller dataset, indicating that QCBMs are more eective in dealing with data and can learn the underlying distribution more eciently than the autoencoder. However, both models can learn the distribution when trained with the full dataset.

Author:

Ahmad Almohamad Alissa

Advisors:

Jonas Stein, Danielle Schumann, Claudia Linnhoff-Popien

Student Thesis | Published April 2023 | Copyright © QAR-Lab
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Efficient Quantum Circuit Architecture for Coined Quantum Walks on many Bipartite Graphs (en)

Efficient Quantum Circuit Architecture for Coined Quantum Walks on many Bipartite Graphs

Abstract:

Quantum walks, a quantum analog of classical random walks, have emerged as a powerful paradigm in quantum computation and simulation. While classical random walks rely on stochastic processes to explore systems, quantum walks leverage the unique properties of quantum mechanics to perform these tasks more efficiently. In particular, discrete-time quantum walks (DTQWs) have been studied extensively for their applications in graph theory, such as graph isomorphism, graph connectivity, and graph-based search problems. Despite their potential, implementing DTQWs on near-term quantum devices remains challenging. While previous works have focused on quantum circuit implementations for DTQWs with uniform coin operators, implementing non-homogeneous coin sets is a complex task that requires new approaches. This thesis presents an efficient quantum circuit architecture for implementing coined DTQWs with non-homogeneous, position-dependent coin sets on a large subset of bipartite graphs. A novel edge labeling scheme, Gray Code Directed Edges encoding, is introduced, taking advantage of Gray code for position encoding and the bipartite structure of the underlying graph to minimize the complexity of the quantum circuits representing coin and shift operators. This optimization leads to fewer gate operations, reducing the impact of noise and errors in near-term quantum devices. A labeling scheme is developed for various graph topologies, including cycle graphs, chained cylinder graphs, and square grid graphs, which are especially relevant for reinforcement learning applications. These findings offer a new perspective on the implementation of coined quantum walks and lay a foundation for future research on quantum walks with non-homogeneous coin sets.

Author:

Viktoryia Patapovich

Advisors:

Jonas Stein, Michael Kölle, Maximilian-Balthasar Mansky, Claudia Linnhoff-Popien

Student Thesis | Published July 2023 | Copyright © QAR-Lab
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Analyzing Reinforcement Learning strategies from a parameterized quantum walker (en)

Analyzing Reinforcement Learning strategies from a parameterized quantum walker

Abstract:

Reinforcement Learning has made significant progress in solving complex problems. Hence, it is not surprising that it can be found in various application domains. Quantum Computing as well is a prospering field, where big advancements could be seen over the last decades. Better quantum computers led to first experimentally proven quantum supremacy. Hence, the field of research grew which led to improvements in various application domains of quantum computing, one of them being quantum Reinforcement Learning where quantum computing is combined with classical reinforcement learning techniques. Among other approaches, quantum walks are used as quantum computational framework which is also the case in the present work. Here, the approach of using parameterized coin matrices to determine the behaviour of the walker adapted to grid graphs is used. Thereby, the parameters of the coin matrices should be learned, such that an optimized performance of the walker to perform a specific task is reached. In this thesis the feasibility of this approach applied to a grid world is investigated using grids of the size 2×2 and 4×4. Furthermore, a new concept for including additional constraints by introducing an extra environment qubit is presented and its influence on the optimization process of the parameters examined. The results can be seen as a proof of concept as for all experiments the approach used here shows better results than the random baseline. Moreover, no negative influence of the environment qubit can be detected. The results gained here are a basis for further research using this approach.

Author:

Lorena Wemmer

Advisors:

Jonas Stein, Michael Kölle, Claudia Linnhoff-Popien

Student Thesis | Published May 2023 | Copyright © QAR-Lab
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