7.2k citations · 13.1k across the 81 of their papers we have counts for
81 papers
Error-tolerant quantum convolutional neural networks for symmetry-protected topological phases
Petr Zapletal, Nathan A. McMahon, Michael J. Hartmann
The analysis of noisy quantum states prepared on current quantum computers is getting beyond the capabilities of classical computing. Quantum neural networks based on parametrized…
Tunable coupler to fully decouple and maximally localize superconducting qubits
Lukas Heunisch, Christopher Eichler, Michael J. Hartmann
Enhancing the capabilities of superconducting quantum hardware, requires higher gate fidelities and lower crosstalk, particularly in larger scale devices, in which qubits are coupl…
Problem specific classical optimization of Hamiltonian simulation
Refik Mansuroglu, Felix Fischer, Michael J. Hartmann
Nonequilibrium time evolution of large quantum systems is a strong candidate for quantum advantage. Variational quantum algorithms have been put forward for this task, but their qu…
Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning
Nico Meyer, Daniel D. Scherer, Axel Plinge +2
Reinforcement learning is a growing field in AI with a lot of potential. Intelligent behavior is learned automatically through trial and error in interaction with the environment.…
Large-scale simulations of Floquet physics on near-term quantum computers
Timo Eckstein, Refik Mansuroglu, Piotr Czarnik +5
Periodically driven quantum systems exhibit a diverse set of phenomena but are more challenging to simulate than their equilibrium counterparts. Here, we introduce the Quantum High…
Deep learning of many-body observables and quantum information scrambling
Naeimeh Mohseni, Junheng Shi, Tim Byrnes +1
Machine learning has shown significant breakthroughs in quantum science, where in particular deep neural networks exhibited remarkable power in modeling quantum many-body systems.…