44 results on '"Nüßlein, Jonas"'
Search Results
2. Emergent cooperation from mutual acknowledgment exchange in multi-agent reinforcement learning
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Phan, Thomy, Sommer, Felix, Ritz, Fabian, Altmann, Philipp, Nüßlein, Jonas, Kölle, Michael, Belzner, Lenz, and Linnhoff-Popien, Claudia
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- 2024
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3. Black Box Optimization Using QUBO and the Cross Entropy Method
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Nüßlein, Jonas, primary, Roch, Christoph, additional, Gabor, Thomas, additional, Stein, Jonas, additional, Linnhoff-Popien, Claudia, additional, and Feld, Sebastian, additional
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- 2023
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4. Solving (Max) 3-SAT via Quadratic Unconstrained Binary Optimization
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Nüßlein, Jonas, primary, Zielinski, Sebastian, additional, Gabor, Thomas, additional, Linnhoff-Popien, Claudia, additional, and Feld, Sebastian, additional
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- 2023
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5. Case-Based Inverse Reinforcement Learning Using Temporal Coherence
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Nüßlein, Jonas, primary, Illium, Steffen, additional, Müller, Robert, additional, Gabor, Thomas, additional, and Linnhoff-Popien, Claudia, additional
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- 2022
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6. ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering
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Müller, Robert, Turalic, Hasan, Phan, Thomy, Kölle, Michael, Nüßlein, Jonas, Linnhoff-Popien, Claudia, Müller, Robert, Turalic, Hasan, Phan, Thomy, Kölle, Michael, Nüßlein, Jonas, and Linnhoff-Popien, Claudia
- Abstract
In the realm of Multi-Agent Reinforcement Learning (MARL), prevailing approaches exhibit shortcomings in aligning with human learning, robustness, and scalability. Addressing this, we introduce ClusterComm, a fully decentralized MARL framework where agents communicate discretely without a central control unit. ClusterComm utilizes Mini-Batch-K-Means clustering on the last hidden layer's activations of an agent's policy network, translating them into discrete messages. This approach outperforms no communication and competes favorably with unbounded, continuous communication and hence poses a simple yet effective strategy for enhancing collaborative task-solving in MARL., Comment: Accepted at ICAART 2024
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- 2024
7. Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting
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Stenzel, Gerhard, Zielinski, Sebastian, Kölle, Michael, Altmann, Philipp, Nüßlein, Jonas, Gabor, Thomas, Stenzel, Gerhard, Zielinski, Sebastian, Kölle, Michael, Altmann, Philipp, Nüßlein, Jonas, and Gabor, Thomas
- Abstract
To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution. Quantum gate matrix caching reduces the overhead of repeated applications of the Kronecker product when applying a gate matrix to the state vector by storing decomposed partial matrices for each gate. Circuit splitting divides the circuit into sub-circuits with fewer gates by constructing a dependency graph, enabling parallel or sequential execution on disjoint subsets of the state vector. These techniques are implemented using the PyTorch machine learning framework. We demonstrate the performance of our approach by comparing it to other PyTorch-compatible quantum state-vector simulators. Our implementation, named Qandle, is designed to seamlessly integrate with existing machine learning workflows, providing a user-friendly API and compatibility with the OpenQASM format. Qandle is an open-source project hosted on GitHub https://github.com/gstenzel/qandle and PyPI https://pypi.org/project/qandle/ .
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- 2024
8. Multi-Agent Quantum Reinforcement Learning Using Evolutionary Optimization
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Kölle, Michael, primary, Topp, Felix, additional, Phan, Thomy, additional, Altmann, Philipp, additional, Nüßlein, Jonas, additional, and Linnhoff-Popien, Claudia, additional
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- 2024
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9. Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection
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Stein, Jonas, primary, Schuman, Daniëlle, additional, Benkard, Magdalena, additional, Holger, Thomas, additional, Sajko, Wanja, additional, Kölle, Michael, additional, Nüßlein, Jonas, additional, Sünkel, Leo, additional, Salomon, Olivier, additional, and Linnhoff-Popien, Claudia, additional
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- 2024
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10. ClusterComm: Discrete Communication in Decentralized MARL Using Internal Representation Clustering
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Müller, Robert, primary, Turalic, Hasan, additional, Phan, Thomy, additional, Kölle, Michael, additional, Nüßlein, Jonas, additional, and Linnhoff-Popien, Claudia, additional
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- 2024
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11. Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
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Stein, Jonas, primary, Poppel, Michael, additional, Adamczyk, Philip, additional, Fabry, Ramona, additional, Wu, Zixin, additional, Kölle, Michael, additional, Nüßlein, Jonas, additional, Schuman, Daniëlle, additional, Altmann, Philipp, additional, Ehmer, Thomas, additional, Narasimhan, Vijay, additional, and Linnhoff-Popien, Claudia, additional
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- 2024
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12. Mapping Quantum Circuits to Modular Architectures with QUBO
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Bandic, Medina, primary, Prielinger, Luise, additional, Nüßlein, Jonas, additional, Ovide, Anabel, additional, Rodrigo, Santiago, additional, Abadal, Sergi, additional, van Someren, Hans, additional, Vardoyan, Gayane, additional, Alarcon, Eduard, additional, Almudever, Carmen G., additional, and Feld, Sebastian, additional
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- 2023
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13. Pattern QUBOs: Algorithmic Construction of 3SAT-to-QUBO Transformations
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Zielinski, Sebastian, primary, Nüßlein, Jonas, additional, Stein, Jonas, additional, Gabor, Thomas, additional, Linnhoff-Popien, Claudia, additional, and Feld, Sebastian, additional
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- 2023
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14. CROP: Towards Distributional-Shift Robust Reinforcement Learning Using Compact Reshaped Observation Processing
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Altmann, Philipp, primary, Ritz, Fabian, additional, Feuchtinger, Leonard, additional, Nüßlein, Jonas, additional, Linnhoff-Popien, Claudia, additional, and Phan, Thomy, additional
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- 2023
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15. NISQ-Ready Community Detection Based on Separation-Node Identification
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Stein, Jonas, primary, Ott, Dominik, additional, Nüßlein, Jonas, additional, Bucher, David, additional, Schönfeld, Mirco, additional, and Feld, Sebastian, additional
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- 2023
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16. Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA
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Stein, Jonas, primary, Chamanian, Farbod, additional, Zorn, Maximilian, additional, Nüßlein, Jonas, additional, Zielinski, Sebastian, additional, Kölle, Michael, additional, and Linnhoff-Popien, Claudia, additional
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- 2023
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17. Influence of Different 3SAT-to-QUBO Transformations on the Solution Quality of Quantum Annealing: A Benchmark Study
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Zielinski, Sebastian, primary, Nüßlein, Jonas, additional, Stein, Jonas, additional, Gabor, Thomas, additional, Linnhoff-Popien, Claudia, additional, and Feld, Sebastian, additional
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- 2023
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18. Mapping quantum circuits to modular architectures with QUBO
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Bandic, Medina, Prielinger, Luise, Nüßlein, Jonas, Ovide González, Anabel, Rodrigo Muñoz, Santiago, Abadal Cavallé, Sergi, van Someren, Hans, Vardoyan, Gayane, Alarcón Cot, Eduardo José, García Almudever, Carmen, Feld, Sebastian, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Bandic, Medina, Prielinger, Luise, Nüßlein, Jonas, Ovide González, Anabel, Rodrigo Muñoz, Santiago, Abadal Cavallé, Sergi, van Someren, Hans, Vardoyan, Gayane, Alarcón Cot, Eduardo José, García Almudever, Carmen, and Feld, Sebastian
- Abstract
Modular quantum computing architectures are a promising alternative to monolithic QPU (Quantum Processing Unit) designs for scaling up quantum devices. They refer to a set of interconnected QPUs or cores consisting of tightly coupled quantum bits that can communicate via quantum-coherent and classical links. In multi-core architectures, it is crucial to minimize the amount of communication between cores when executing an algorithm. Therefore, mapping a quantum circuit onto a modular architecture involves finding an optimal assignment of logical qubits (qubits in the quantum circuit) to different cores with the aim to minimize the number of expensive inter-core operations while adhering to given hardware constraints. In this paper, we propose for the first time a Quadratic Unconstrained Binary Optimization (QUBO) technique to encode the problem and the solution for both qubit allocation and inter-core communication costs in binary decision variables. To this end, the quantum circuit is split into slices, and qubit assignment is formulated as a graph partitioning problem for each circuit slice. The costly inter-core communication is reduced by penalizing inter-core qubit communications. The final solution is obtained by minimizing the overall cost across all circuit slices. To evaluate the effectiveness of our approach, we conduct a detailed analysis using a representative set of benchmarks having a high number of qubits on two different multi-core architectures. Our method showed promising results and performed exceptionally well with very dense and highly-parallelized circuits that require on average 0.78 inter-core communications per two-qubit gate., MB and SF would like to acknowledge funding from Intel Corporation. EA and CGA acknowledge support from the EU, grant HORIZON-EIC-2022-PATHFINDEROPEN-01-101099697 (QUADRATURE). SA acknowledges support from the EU, grant HORIZON-ERC-2021-101042080 (WINC) and GV acknoledges support from NWO QSC grant BGR2 17.269, Peer Reviewed, Postprint (author's final draft)
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- 2023
19. Influence of Different 3SAT-to-QUBO Transformations on the Solution Quality of Quantum Annealing: A Benchmark Study
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Zielinski, Sebastian (author), Gabor, Thomas (author), Nüßlein, Jonas (author), Linnhoff-Popien, Claudia (author), Stein, Jonas (author), Feld, S. (author), Zielinski, Sebastian (author), Gabor, Thomas (author), Nüßlein, Jonas (author), Linnhoff-Popien, Claudia (author), Stein, Jonas (author), and Feld, S. (author)
- Abstract
To solve 3sat instances on quantum annealers they need to be transformed to an instance of Quadratic Unconstrained Binary Optimization (QUBO). When there are multiple transformations available, the question arises whether different transformations lead to differences in the obtained solution quality. Thus, in this paper we conduct an empirical benchmark study, in which we compare four structurally different QUBO transformations for the 3sat problem with regards to the solution quality on D-Wave’s Advantage_system4.1. We show that the choice of QUBO transformation can significantly impact the number of correct solutions the quantum annealer returns. Furthermore, we show that the size of a QUBO instance (i.e., the dimension of the QUBO matrix) is not a sufficient predictor for solution quality, as larger QUBO instances may produce better results than smaller QUBO instances for the same problem. We also empirically show that the number of different quadratic values of a QUBO instance, combined with their range, can significantly impact the solution quality., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Quantum Circuit Architectures and Technology
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- 2023
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20. Pattern QUBOs: Algorithmic Construction of 3SAT-to-QUBO Transformations
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Zielinski, Sebastian (author), Nüßlein, Jonas (author), Stein, Jonas (author), Gabor, Thomas (author), Linnhoff-Popien, Claudia (author), Feld, S. (author), Zielinski, Sebastian (author), Nüßlein, Jonas (author), Stein, Jonas (author), Gabor, Thomas (author), Linnhoff-Popien, Claudia (author), and Feld, S. (author)
- Abstract
One way of solving 3sat instances on a quantum computer is to transform the 3sat instances into instances of Quadratic Unconstrained Binary Optimizations (QUBOs), which can be used as an input for the QAOA algorithm on quantum gate systems or as an input for quantum annealers. This mapping is performed by a 3sat-to-QUBO transformation. Recently, it has been shown that the choice of the 3sat-to-QUBO transformation can significantly impact the solution quality of quantum annealing. It has been shown that the solution quality can vary up to an order of magnitude difference in the number of correct solutions received, depending solely on the 3sat-to-QUBO transformation. An open question is: what causes these differences in the solution quality when solving 3sat-instances with different 3sat-to-QUBO transformations? To be able to conduct meaningful studies that assess the reasons for the differences in the performance, a larger number of different 3sat-to-QUBO transformations would be needed. However, currently, there are only a few known 3sat-to-QUBO transformations, and all of them were created manually by experts, who used time and clever reasoning to create these transformations. In this paper, we will solve this problem by proposing an algorithmic method that is able to create thousands of new and different 3sat-to-QUBO transformations, and thus enables researchers to systematically study the reasons for the significant difference in the performance of different 3sat-to-QUBO transformations. Our algorithmic method is an exhaustive search procedure that exploits properties of (Formula presented.) dimensional pattern QUBOs, a concept which has been used implicitly in the creation of 3sat-to-QUBO transformations before, but was never described explicitly. We will thus also formally and explicitly introduce the concept of pattern QUBOs in this paper., Quantum Circuit Architectures and Technology
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- 2023
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21. NISQ-Ready Community Detection Based on Separation-Node Identification
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Stein, Jonas (author), Ott, Dominik (author), Nüßlein, Jonas (author), Bucher, David (author), Schönfeld, Mirco (author), Feld, S. (author), Stein, Jonas (author), Ott, Dominik (author), Nüßlein, Jonas (author), Bucher, David (author), Schönfeld, Mirco (author), and Feld, S. (author)
- Abstract
The analysis of network structure is essential to many scientific areas ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO-based approach that only needs number-of-nodes qubits and is represented by a QUBO matrix as sparse as the input graph’s adjacency matrix. The substantial improvement in the sparsity of the QUBO matrix, which is typically very dense in related work, is achieved through the novel concept of separation nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which, upon its removal from the graph, yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept by achieving an up to 95% optimal solution quality on three established real-world benchmark datasets. This work hence displays a promising approach to NISQ-ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large-scale, real-world problem instances., Quantum Circuit Architectures and Technology
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- 2023
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22. Solving (Max) 3-SAT via Quadratic Unconstrained Binary Optimization
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Nüßlein, Jonas, Zielinski, Sebastian, Gabor, Thomas, Linnhoff-Popien, Claudia, Feld, Sebastian, Nüßlein, Jonas, Zielinski, Sebastian, Gabor, Thomas, Linnhoff-Popien, Claudia, and Feld, Sebastian
- Abstract
We introduce a novel approach to translate arbitrary 3-SAT instances to Quadratic Unconstrained Binary Optimization (QUBO) as they are used by quantum annealing (QA) or the quantum approximate optimization algorithm (QAOA). Our approach requires fewer couplings and fewer physical qubits than the current state-of-the-art, which results in higher solution quality. We verified the practical applicability of the approach by testing it on a D-Wave quantum annealer.
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- 2023
23. Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability
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Phan, Thomy, Ritz, Fabian, Altmann, Philipp, Zorn, Maximilian, Nüßlein, Jonas, Kölle, Michael, Gabor, Thomas, Linnhoff-Popien, Claudia, Phan, Thomy, Ritz, Fabian, Altmann, Philipp, Zorn, Maximilian, Nüßlein, Jonas, Kölle, Michael, Gabor, Thomas, and Linnhoff-Popien, Claudia
- Abstract
Stochastic partial observability poses a major challenge for decentralized coordination in multi-agent reinforcement learning but is largely neglected in state-of-the-art research due to a strong focus on state-based centralized training for decentralized execution (CTDE) and benchmarks that lack sufficient stochasticity like StarCraft Multi-Agent Challenge (SMAC). In this paper, we propose Attention-based Embeddings of Recurrence In multi-Agent Learning (AERIAL) to approximate value functions under stochastic partial observability. AERIAL replaces the true state with a learned representation of multi-agent recurrence, considering more accurate information about decentralized agent decisions than state-based CTDE. We then introduce MessySMAC, a modified version of SMAC with stochastic observations and higher variance in initial states, to provide a more general and configurable benchmark regarding stochastic partial observability. We evaluate AERIAL in Dec-Tiger as well as in a variety of SMAC and MessySMAC maps, and compare the results with state-based CTDE. Furthermore, we evaluate the robustness of AERIAL and state-based CTDE against various stochasticity configurations in MessySMAC., Comment: Accepted to ICML 2023
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- 2023
24. Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
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Stein, Jonas, Poppel, Michael, Adamczyk, Philip, Fabry, Ramona, Wu, Zixin, Kölle, Michael, Nüßlein, Jonas, Schuman, Daniëlle, Altmann, Philipp, Ehmer, Thomas, Narasimhan, Vijay, Linnhoff-Popien, Claudia, Stein, Jonas, Poppel, Michael, Adamczyk, Philip, Fabry, Ramona, Wu, Zixin, Kölle, Michael, Nüßlein, Jonas, Schuman, Daniëlle, Altmann, Philipp, Ehmer, Thomas, Narasimhan, Vijay, and Linnhoff-Popien, Claudia
- Abstract
Surrogate models are ubiquitously used in industry and academia to efficiently approximate given black box functions. As state-of-the-art methods from classical machine learning frequently struggle to solve this problem accurately for the often scarce and noisy data sets in practical applications, investigating novel approaches is of great interest. Motivated by recent theoretical results indicating that quantum neural networks (QNNs) have the potential to outperform their classical analogs in the presence of scarce and noisy data, we benchmark their qualitative performance for this scenario empirically. Our contribution displays the first application-centered approach of using QNNs as surrogate models on higher dimensional, real world data. When compared to a classical artificial neural network with a similar number of parameters, our QNN demonstrates significantly better results for noisy and scarce data, and thus motivates future work to explore this potential quantum advantage in surrogate modelling. Finally, we demonstrate the performance of current NISQ hardware experimentally and estimate the gate fidelities necessary to replicate our simulation results.
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- 2023
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25. Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection
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Stein, Jonas, Schuman, Daniëlle, Benkard, Magdalena, Holger, Thomas, Sajko, Wanja, Kölle, Michael, Nüßlein, Jonas, Sünkel, Leo, Salomon, Olivier, Linnhoff-Popien, Claudia, Stein, Jonas, Schuman, Daniëlle, Benkard, Magdalena, Holger, Thomas, Sajko, Wanja, Kölle, Michael, Nüßlein, Jonas, Sünkel, Leo, Salomon, Olivier, and Linnhoff-Popien, Claudia
- Abstract
Anomaly detection in Endpoint Detection and Response (EDR) is a critical task in cybersecurity programs of large companies. With rapidly growing amounts of data and the omnipresence of zero-day attacks, manual and rule-based detection techniques are no longer eligible in practice. While classical machine learning approaches to this problem exist, they frequently show unsatisfactory performance in differentiating malicious from benign anomalies. A promising approach to attain superior generalization than currently employed machine learning techniques are quantum generative models. Allowing for the largest representation of data on available quantum hardware, we investigate Quantum Annealing based Quantum Boltzmann Machines (QBMs) for the given problem. We contribute the first fully unsupervised approach for the problem of anomaly detection using QBMs and evaluate its performance on an EDR inspired synthetic dataset. Our results indicate that QBMs can outperform their classical analog (i.e., Restricted Boltzmann Machines) in terms of result quality and training steps in special cases. When employing Quantum Annealers from D-Wave Systems, we conclude that either more accurate classical simulators or substantially more QPU time is needed to conduct the necessary hyperparameter optimization allowing to replicate our simulation results on quantum hardware.
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- 2023
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26. Mapping quantum circuits to modular architectures with QUBO
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Bandic, Medina, Prielinger, Luise, Nüßlein, Jonas, Ovide, Anabel, Rodrigo, Santiago, Abadal, Sergi, van Someren, Hans, Vardoyan, Gayane, Alarcon, Eduard, Almudever, Carmen G., Feld, Sebastian, Bandic, Medina, Prielinger, Luise, Nüßlein, Jonas, Ovide, Anabel, Rodrigo, Santiago, Abadal, Sergi, van Someren, Hans, Vardoyan, Gayane, Alarcon, Eduard, Almudever, Carmen G., and Feld, Sebastian
- Abstract
Modular quantum computing architectures are a promising alternative to monolithic QPU (Quantum Processing Unit) designs for scaling up quantum devices. They refer to a set of interconnected QPUs or cores consisting of tightly coupled quantum bits that can communicate via quantum-coherent and classical links. In multi-core architectures, it is crucial to minimize the amount of communication between cores when executing an algorithm. Therefore, mapping a quantum circuit onto a modular architecture involves finding an optimal assignment of logical qubits (qubits in the quantum circuit) to different cores with the aim to minimize the number of expensive inter-core operations while adhering to given hardware constraints. In this paper, we propose for the first time a Quadratic Unconstrained Binary Optimization (QUBO) technique to encode the problem and the solution for both qubit allocation and inter-core communication costs in binary decision variables. To this end, the quantum circuit is split into slices, and qubit assignment is formulated as a graph partitioning problem for each circuit slice. The costly inter-core communication is reduced by penalizing inter-core qubit communications. The final solution is obtained by minimizing the overall cost across all circuit slices. To evaluate the effectiveness of our approach, we conduct a detailed analysis using a representative set of benchmarks having a high number of qubits on two different multi-core architectures. Our method showed promising results and performed exceptionally well with very dense and highly-parallelized circuits that require on average 0.78 inter-core communications per two-qubit gate., Comment: Submitted to IEEE QCE 2023
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- 2023
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27. Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA
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Stein, Jonas, Chamanian, Farbod, Zorn, Maximilian, Nüßlein, Jonas, Zielinski, Sebastian, Kölle, Michael, Linnhoff-Popien, Claudia, Stein, Jonas, Chamanian, Farbod, Zorn, Maximilian, Nüßlein, Jonas, Zielinski, Sebastian, Kölle, Michael, and Linnhoff-Popien, Claudia
- Abstract
Quantum computing provides powerful algorithmic tools that have been shown to outperform established classical solvers in specific optimization tasks. A core step in solving optimization problems with known quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) is the problem formulation. While quantum optimization has historically centered around Quadratic Unconstrained Optimization (QUBO) problems, recent studies show, that many combinatorial problems such as the TSP can be solved more efficiently in their native Polynomial Unconstrained Optimization (PUBO) forms. As many optimization problems in practice also contain continuous variables, our contribution investigates the performance of the QAOA in solving continuous optimization problems when using PUBO and QUBO formulations. Our extensive evaluation on suitable benchmark functions, shows that PUBO formulations generally yield better results, while requiring less qubits. As the multi-qubit interactions needed for the PUBO variant have to be decomposed using the hardware gates available, i.e., currently single- and two-qubit gates, the circuit depth of the PUBO approach outscales its QUBO alternative roughly linearly in the order of the objective function. However, incorporating the planned addition of native multi-qubit gates such as the global Molmer-Sorenson gate, our experiments indicate that PUBO outperforms QUBO for higher order continuous optimization problems in general.
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- 2023
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28. CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
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Altmann, Philipp, Ritz, Fabian, Feuchtinger, Leonard, Nüßlein, Jonas, Linnhoff-Popien, Claudia, Phan, Thomy, Altmann, Philipp, Ritz, Fabian, Feuchtinger, Leonard, Nüßlein, Jonas, Linnhoff-Popien, Claudia, and Phan, Thomy
- Abstract
The safe application of reinforcement learning (RL) requires generalization from limited training data to unseen scenarios. Yet, fulfilling tasks under changing circumstances is a key challenge in RL. Current state-of-the-art approaches for generalization apply data augmentation techniques to increase the diversity of training data. Even though this prevents overfitting to the training environment(s), it hinders policy optimization. Crafting a suitable observation, only containing crucial information, has been shown to be a challenging task itself. To improve data efficiency and generalization capabilities, we propose Compact Reshaped Observation Processing (CROP) to reduce the state information used for policy optimization. By providing only relevant information, overfitting to a specific training layout is precluded and generalization to unseen environments is improved. We formulate three CROPs that can be applied to fully observable observation- and action-spaces and provide methodical foundation. We empirically show the improvements of CROP in a distributionally shifted safety gridworld. We furthermore provide benchmark comparisons to full observability and data-augmentation in two different-sized procedurally generated mazes., Comment: 9 pages, 5 figures, published at IJCAI 2023
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- 2023
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29. Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization
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Kölle, Michael, Topp, Felix, Phan, Thomy, Altmann, Philipp, Nüßlein, Jonas, Linnhoff-Popien, Claudia, Kölle, Michael, Topp, Felix, Phan, Thomy, Altmann, Philipp, Nüßlein, Jonas, and Linnhoff-Popien, Claudia
- Abstract
Multi-Agent Reinforcement Learning is becoming increasingly more important in times of autonomous driving and other smart industrial applications. Simultaneously a promising new approach to Reinforcement Learning arises using the inherent properties of quantum mechanics, reducing the trainable parameters of a model significantly. However, gradient-based Multi-Agent Quantum Reinforcement Learning methods often have to struggle with barren plateaus, holding them back from matching the performance of classical approaches. We build upon an existing approach for gradient free Quantum Reinforcement Learning and propose three genetic variations with Variational Quantum Circuits for Multi-Agent Reinforcement Learning using evolutionary optimization. We evaluate our genetic variations in the Coin Game environment and also compare them to classical approaches. We showed that our Variational Quantum Circuit approaches perform significantly better compared to a neural network with a similar amount of trainable parameters. Compared to the larger neural network, our approaches archive similar results using $97.88\%$ less parameters.
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- 2023
30. Improving Primate Sounds Classification using Binary Presorting for Deep Learning
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Kölle, Michael, Illium, Steffen, Zorn, Maximilian, Nüßlein, Jonas, Suchostawski, Patrick, Linnhoff-Popien, Claudia, Kölle, Michael, Illium, Steffen, Zorn, Maximilian, Nüßlein, Jonas, Suchostawski, Patrick, and Linnhoff-Popien, Claudia
- Abstract
In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular. Unfortunately, available datasets from this field of research are often not optimal learning material; Samples can be weakly labeled, of different lengths or come with a poor signal-to-noise ratio. In this work, we introduce a generalized approach that first relabels subsegments of MEL spectrogram representations, to achieve higher performances on the actual multi-class classification tasks. For both the binary pre-sorting and the classification, we make use of convolutional neural networks (CNN) and various data-augmentation techniques. We showcase the results of this approach on the challenging \textit{ComparE 2021} dataset, with the task of classifying between different primate species sounds, and report significantly higher Accuracy and UAR scores in contrast to comparatively equipped model baselines., Comment: DeLTA
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- 2023
31. Quantum Surrogate Modeling for Chemical and Pharmaceutical Development
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Stein, Jonas, Poppel, Michael, Adamczyk, Philip, Fabry, Ramona, Wu, Zixin, Kölle, Michael, Nüßlein, Jonas, Schuman, Daniëlle, Altmann, Philipp, Ehmer, Thomas, Narasimhan, Vijay, and Linnhoff-Popien, Claudia
- Subjects
FOS: Computer and information sciences ,Quantum Physics ,Emerging Technologies (cs.ET) ,FOS: Physical sciences ,Computer Science - Emerging Technologies ,Quantum Physics (quant-ph) - Abstract
A central problem of development in chemical and pharmaceutical industries is modelling a cheap to evaluate surrogate function, that approximates a given black box function sufficiently well. As state-of-the-art methods from classical machine learning struggle to solve this problem accurately for the typically scarce and noisy datasets in practical applications, investigating novel approaches is of great interest to chemical companies worldwide. We demonstrate that quantum neural networks (QNNs) offer a particularly promising approach to this issue and experimentally support recent theoretical findings indicating their potential to outperform classical equivalents in training on small datasets and noisy data. Our contribution displays the first application centered exploration of using QNNs as surrogate models on higher dimensional, realistic data. In extensive experiments, our QNN significantly outperforms a minimalist classical artificial neural network on noisy and scarce data, displaying a possible advantage of quantum surrogate models empirically. Finally, we demonstrate the performance of current NISQ hardware experimentally and estimate the gate fidelities necessary to replicate our simulation results.
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- 2023
32. VoronoiPatches: Evaluating a New Data Augmentation Method
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Illium, Steffen, primary, Griffin, Gretchen, additional, Kölle, Michael, additional, Zorn, Maximilian, additional, Nüßlein, Jonas, additional, and Linnhoff-Popien, Claudia, additional
- Published
- 2023
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33. Enhancing Quantum Algorithms for Quadratic Unconstrained Binary Optimization via Integer Programming
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Wagner, Friedrich, Nüßlein, Jonas, and Liers, Frauke
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Quantum Physics ,FOS: Physical sciences ,Quantum Physics (quant-ph) - Abstract
To date, research in quantum computation promises potential for outperforming classical heuristics in combinatorial optimization. However, when aiming at provable optimality, one has to rely on classical exact methods like integer programming. State-of-the-art integer programming algorithms can compute strong relaxation bounds even for hard instances, but may have to enumerate a large number of subproblems for determining an optimum solution. If the potential of quantum computing realizes, it can be expected that in particular finding high-quality solutions for hard problems can be done fast. Still, near-future quantum hardware considerably limits the size of treatable problems. In this work, we go one step into integrating the potentials of quantum and classical techniques for combinatorial optimization. We propose a hybrid heuristic for the weighted maximum-cut problem or, equivalently, for quadratic unconstrained binary optimization. The heuristic employs a linear programming relaxation, rendering it well-suited for integration into exact branch-and-cut algorithms. For large instances, we reduce the problem size according to a linear relaxation such that the reduced problem can be handled by quantum machines of limited size. Moreover, we improve the applicability of QAOA, a parameterized quantum algorithm, by deriving optimal parameters for special instances which motivates a parameter estimate for arbitrary instances. We present numerous computational results from real quantum hardware.
- Published
- 2023
34. Influence of Different 3SAT-to-QUBO Transformations on the Solution Quality of Quantum Annealing: A Benchmark Study
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Zielinski, Sebastian, Nüßlein, Jonas, Stein, Jonas, Gabor, Thomas, Linnhoff-Popien, Claudia, and Feld, Sebastian
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Quantum Physics ,FOS: Physical sciences ,Quantum Physics (quant-ph) - Abstract
To solve 3SAT instances on quantum annealers they need to be transformed to an instance of Quadratic Unconstrained Binary Optimization (QUBO). When there are multiple transformations available, the question arises whether different transformations lead to differences in the obtained solution quality. Thus, in this paper we conduct an empirical benchmark study, in which we compare four structurally different QUBO transformations for the 3SAT problem with regards to the solution quality on D-Wave's Advantage_system4.1. We show that the choice of QUBO transformation can significantly impact the number of correct solutions the quantum annealer returns. Furthermore, we show that the size of a QUBO instance (i.e., the dimension of the QUBO matrix) is not a sufficient predictor for solution quality, as larger QUBO instances may produce better results than smaller QUBO instances for the same problem. We also empirically show that the number of different quadratic values of a QUBO instance, combined with their range, can significantly impact the solution quality.
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- 2023
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35. Pattern QUBOs: Algorithmic construction of 3SAT-to-QUBO transformations
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Zielinski, Sebastian, Nüßlein, Jonas, Stein, Jonas, Gabor, Thomas, Linnhoff-Popien, Claudia, and Feld, Sebastian
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Quantum Physics ,FOS: Physical sciences ,Quantum Physics (quant-ph) - Abstract
3SAT instances need to be transformed into instances of Quadratic Unconstrained Binary Optimization (QUBO) to be solved on a quantum annealer. Although it has been shown that the choice of the 3SAT-to-QUBO transformation can impact the solution quality of quantum annealing significantly, currently only a few 3SAT-to-QUBO transformations are known. Additionally, all of the known 3SAT-to-QUBO transformations were created manually (and not procedurally) by an expert using reasoning, which is a rather slow and limiting process. In this paper, we will introduce the name Pattern QUBO for a concept that has been used implicitly in the construction of 3SAT-to-QUBO transformations before. We will provide an in-depth explanation for the idea behind Pattern QUBOs and show its importance by proposing an algorithmic method that uses Pattern QUBOs to create new 3SAT-to-QUBO transformations automatically. As an additional application of Pattern QUBOs and our proposed algorithmic method, we introduce approximate 3SAT-to-QUBO transformations. These transformations sacrifice optimality but use significantly fewer variables (and thus physical qubits on quantum hardware) than non-approximate 3SAT-to-QUBO transformations. We will show that approximate 3SAT-to-QUBO transformations can nevertheless be very effective in some cases.
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- 2023
- Full Text
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36. The Effect of Penalty Factors of Constrained Hamiltonians on the Eigenspectrum in Quantum Annealing
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Roch, Christoph, primary, Ratke, Daniel, additional, Nüßlein, Jonas, additional, Gabor, Thomas, additional, and Feld, Sebastian, additional
- Published
- 2022
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37. Emergent Cooperation from Mutual Acknowledgment Exchange in Multi-Agent Reinforcement Learning
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Phan, Thomy, primary, Sommer, Felix, additional, Ritz, Fabian, additional, Altmann, Philipp, additional, Nüßlein, Jonas, additional, Kölle, Michael, additional, Belzner, Lenz, additional, and Linnhoff-Popien, Claudia, additional
- Published
- 2022
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38. Algorithmic QUBO formulations for k -SAT and hamiltonian cycles
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Nüßlein, Jonas, primary, Gabor, Thomas, additional, Linnhoff-Popien, Claudia, additional, and Feld, Sebastian, additional
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- 2022
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39. Case-Based Inverse Reinforcement Learning Using Temporal Coherence
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Nüßlein, Jonas, Illium, Steffen, Müller, Robert, Gabor, Thomas, and Linnhoff-Popien, Claudia
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Machine Learning (cs.LG) - Abstract
Providing expert trajectories in the context of Imitation Learning is often expensive and time-consuming. The goal must therefore be to create algorithms which require as little expert data as possible. In this paper we present an algorithm that imitates the higher-level strategy of the expert rather than just imitating the expert on action level, which we hypothesize requires less expert data and makes training more stable. As a prior, we assume that the higher-level strategy is to reach an unknown target state area, which we hypothesize is a valid prior for many domains in Reinforcement Learning. The target state area is unknown, but since the expert has demonstrated how to reach it, the agent tries to reach states similar to the expert. Building on the idea of Temporal Coherence, our algorithm trains a neural network to predict whether two states are similar, in the sense that they may occur close in time. During inference, the agent compares its current state with expert states from a Case Base for similarity. The results show that our approach can still learn a near-optimal policy in settings with very little expert data, where algorithms that try to imitate the expert at the action level can no longer do so., accepted at ICCBR
- Published
- 2022
40. Algorithmic QUBO formulations for k-SAT and hamiltonian cycles
- Author
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Nüßlein, Jonas (author), Gabor, Thomas (author), Linnhoff-Popien, Claudia (author), Feld, S. (author), Nüßlein, Jonas (author), Gabor, Thomas (author), Linnhoff-Popien, Claudia (author), and Feld, S. (author)
- Abstract
Quadratic Unconstrained Binary Optimization (QUBO) can be seen as a generic language for optimization problems. QUBOs attract particular attention since they can be solved with quantum hardware, like quantum annealers or quantum gate computers running QAOA. In this paper, we present two novel QUBO formulations for k-SAT and Hamiltonian Cycles that scale significantly better than existing approaches. For k-SAT we reduce the growth of the QUBO matrix from O(k) to O(log(k)). For Hamiltonian Cycles the matrix no longer grows quadratically in the number of nodes, as currently, but linearly in the number of edges and logarithmically in the number of nodes. We present these two formulations not as mathematical expressions, as most QUBO formulations are, but as meta-algorithms that facilitate the design of more complex QUBO formulations and allow easy reuse in larger and more complex QUBO formulations., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Quantum Circuit Architectures and Technology
- Published
- 2022
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41. NISQ-ready community detection based on separation-node identification
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Stein, Jonas, Ott, Dominik, Nüßlein, Jonas, Bucher, David, Schoenfeld, Mirco, Feld, Sebastian, Stein, Jonas, Ott, Dominik, Nüßlein, Jonas, Bucher, David, Schoenfeld, Mirco, and Feld, Sebastian
- Abstract
The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which -- upon its removal from the graph -- yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.
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- 2022
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42. VoronoiPatches: Evaluating A New Data Augmentation Method
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Illium, Steffen, Griffin, Gretchen, Kölle, Michael, Zorn, Maximilian, Nüßlein, Jonas, Linnhoff-Popien, Claudia, Illium, Steffen, Griffin, Gretchen, Kölle, Michael, Zorn, Maximilian, Nüßlein, Jonas, and Linnhoff-Popien, Claudia
- Abstract
Overfitting is a problem in Convolutional Neural Networks (CNN) that causes poor generalization of models on unseen data. To remediate this problem, many new and diverse data augmentation methods (DA) have been proposed to supplement or generate more training data, and thereby increase its quality. In this work, we propose a new data augmentation algorithm: VoronoiPatches (VP). We primarily utilize non-linear recombination of information within an image, fragmenting and occluding small information patches. Unlike other DA methods, VP uses small convex polygon-shaped patches in a random layout to transport information around within an image. Sudden transitions created between patches and the original image can, optionally, be smoothed. In our experiments, VP outperformed current DA methods regarding model variance and overfitting tendencies. We demonstrate data augmentation utilizing non-linear re-combination of information within images, and non-orthogonal shapes and structures improves CNN model robustness on unseen data.
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- 2022
43. Black Box Optimization Using QUBO and the Cross Entropy Method
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Nüßlein, Jonas, Roch, Christoph, Gabor, Thomas, Stein, Jonas, Linnhoff-Popien, Claudia, Feld, Sebastian, Nüßlein, Jonas, Roch, Christoph, Gabor, Thomas, Stein, Jonas, Linnhoff-Popien, Claudia, and Feld, Sebastian
- Abstract
Black-box optimization (BBO) can be used to optimize functions whose analytic form is unknown. A common approach to realising BBO is to learn a surrogate model which approximates the target black-box function which can then be solved via white-box optimization methods. In this paper, we present our approach BOX-QUBO, where the surrogate model is a QUBO matrix. However, unlike in previous state-of-the-art approaches, this matrix is not trained entirely by regression, but mostly by classification between 'good' and 'bad' solutions. This better accounts for the low capacity of the QUBO matrix, resulting in significantly better solutions overall. We tested our approach against the state-of-the-art on four domains and in all of them BOX-QUBO showed better results. A second contribution of this paper is the idea to also solve white-box problems, i.e. problems which could be directly formulated as QUBO, by means of black-box optimization in order to reduce the size of the QUBOs to the information-theoretic minimum. Experiments show that this significantly improves the results for MAX-k-SAT.
- Published
- 2022
44. Frequent Itemset Mining using QUBO
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Nüßlein, Jonas
- Subjects
FOS: Computer and information sciences ,Computer Science - Databases ,Databases (cs.DB) - Abstract
In this paper we propose a R-step approximation to solve frequent itemset mining on quantum hardware like quantum annealing or QAOA. The idea is to search for the set of items where the minimal 2-item frequency is maximal. This can be represented as a maximum clique problem.
- Published
- 2019
- Full Text
- View/download PDF
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