8 papers · 1 filter
Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification
Daniëlle Schuman, Mark V. Seebode, Tobias Rohe +5
Exploiting the fact that samples drawn from a quantum annealer inherently follow a Boltzmann-like distribution, annealing-based Quantum Boltzmann Machines (QBMs) have gained increa…
Clique detection using symmetry-restricted quantum circuits
Maximilian Balthasar Mansky, Tobias Rohe, Dmytro Bondarenko +2
We show the application of permutation-invariant quantum circuits to the clique problem. The experiment asks to label a clique through identification of the nodes in a larger subgr…
Analysis of quantum neural network performance via edge cases
Maximilian Balthasar Mansky, Tobias Rohe, Linus Menzel +2
We evaluate the particular performance of different quantum machine learning networks on a graph classification task. Quantum circuits with varying internal symmetry that completel…
Scaling of symmetry-restricted quantum circuits
Maximilian Balthasar Mansky, Miguel Armayor Martinez, Alejandro Bravo de la Serna +6
The intrinsic symmetries of physical systems have been employed to reduce the number of degrees of freedom of systems, thereby simplifying computations. In this work, we investigat…
Solving graph problems using permutation-invariant quantum machine learning
Maximilian Balthasar Mansky, Tobias Rohe, Gerhard Stenzel +7
Many computational problems are unchanged under some symmetry operation. In classical machine learning, this can be reflected with the layer structure of the neural network. In qua…
Quality Diversity for Variational Quantum Circuit Optimization
Maximilian Zorn, Jonas Stein, Maximilian Balthasar Mansky +3
Optimizing the architecture of variational quantum circuits (VQCs) is crucial for advancing quantum computing (QC) towards practical applications. Current methods range from static…