Optimizing the architecture of variational quantum circuits (VQCs) is crucial for advancing quantum computing (QC) towards practical applications. Current methods range from static ansatz design and...
Reinforcement Learning (RL) policies are designed to predict actions based on current observations to maximize cumulative future rewards. In real-world applications (i.e., non-simulated...
The demand for artificially generated data for the development, training and testing of new algorithms is omnipresent. Quantum computing (QC), does offer the hope that its inherent probabilistic...
Training the Variational Quantum Eigensolver (VQE) is a task that requires substantial compute. We propose the use of concepts from transfer learning to considerably reduce the training time when...
On account of the inherent complexity and novelty of quantum computing (QC), as well as the expected lack of expertise of many of the stakeholders involved in its development, QC software development...
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...
Quantum machine learning offers novel paradigms to address limitations in traditional natural language processing models, such as fixed context lengths and computational inefficiencies. In this work,...
We introduce a novel quantum algorithm for determining graph connectedness using a constant number of measurements. The algorithm can be extended to find connected components with a linear number of...
Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and advantages. Hybrid...
Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large...
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...
As an application domain where the slightest qualitative improvements can yield immense value, finance is a promising candidate for early quantum advantage. Focusing on the rapidly advancing field of...
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...
Many promising applications of quantum computing with a provable speedup center around the HHL algorithm. Due to restrictions on the hardware and its significant demand on qubits and gates in known...
Practical quantum computing (QC) is still in its in-fancy and problems considered are usually fairly small, especially in quantum machine learning when compared to its classical counterpart. Image...
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...
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...
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...
Reinforcement learning has driven impressive advances in machine learning. Simultaneously, quantum-enhanced machine learning algorithms using quantum annealing underlie heavy developments. Recently,...
CSG trees are an intuitive, yet powerful technique for the representation of geometry using a combination of Boolean set-operations and geometric primitives. In general, there exists an infinite...
Modifying the quantum-assisted genetic algorithm Thomas Gabor, Michael Lachner, Nico Kraus, Christoph Roch, Jonas Stein, Daniel Ratke, Claudia Linnhoff-Popien Abstract Based on the quantum-assisted...
Internet traffic is constantly increasing over time due to growing digitization and the increasing use of bandwidth intensive applications. Internet consumers, be it large industry, small enterprises...
Quantum Annealing is an algorithm for solving instances of quadratic unconstrained binary optimization (QUBO) that is implemented in hardware utilizing quantum effects to quickly find approximate...
A Quantum Annealing Algorithm for Finding Pure Nash Equilibria in Graphical Games Christoph Roch, Thomy Phan, Sebastian Feld, Robert Müller, Thomas Gabor, Carsten Hahn, Claudia Linnhoff-Popien...
Approximating Archetypal Analysis Using Quantum Annealing S. Feld, C. Roch, K. Geirhos, and T. Gabor Abstract Archetypes are those extreme values of a data set that can jointly represent all other...
The Dynamic Time Warping Distance Measure as QUBO Formulation S. Feld, C. Roch, T. Gabor, M. To, and C. Linnhoff-Popien Abstract Dynamic Time Warping (DTW) is a representative of a distance measure...
Insights on Training Neural Networks for QUBO Tasks T. Gabor, S. Feld, H. Safi, T. Phan, and C. Linnhoff-Popien Abstract Current hardware limitations restrict the potential when solving quadratic...
We discuss the synergetic connection between quantum computing and artificial intelligence. After surveying current approaches to quantum artificial intelligence and relating them to a formal model...
In this paper, we explore how problems’ approximate versions of varying degree can be systematically constructed for quantum annealer programs, and how this influences result quality or the...
We describe existing Ising formulations for the maximum clique search problem and the smallest exact cover problem, both of which are important building blocks of the proposed compression pipeline....
We experimentally investigate if and how the degree of approximability influences implementation and run-time performance. Our experiments indicate a discrepancy between classical approximation...
In this work we propose a system architecture for the integration of quantum accelerators. In order to evaluate our proposed system architecture we implemented various algorithms including a...
In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical optimizer to find better parameter more easily and hence results in an improved performance. We...
This work presents a quantum-classic hybrid solution method for the CVRP. It clarifies whether the implementation of such a method pays off in comparison to existing classical solution methods...
This book comprises a section containing a keynote and four sections with scientific papers. The sessions deal with the following topics that are crucial to the development of future improvements in...
We show that the phase transition regarding the computational complexity of the problem, which is well-known to occur for 3SAT on classical machines (where it causes a detrimental increase in...
