Below are the publications indexed by the CRIS.
You may also consult the pre-prints on the arXiv.
- Sarma, B, Hartmann, M. Designing fast quantum gates using optimal control with a reinforcement-learning ansatz Physical Review Applied (2025) doi:10.1103/PhysRevApplied.23.014015
- Marti, L, Mansuroglu, R, Hartmann, M. Efficient Quantum Cooling Algorithm for Fermionic Systems Quantum (2025) doi:10.22331/q-2025-02-18-1635
- Nützel, L, Gresch, A, Hehn, L, Marti, L, et al. Solving an industrially relevant quantum chemistry problem on quantum hardware Quantum Science and Technology (2025) doi:10.1088/2058-9565/ad9ed3
- Mohseni, N, Shi, J, Byrnes, T, Hartmann, M. Deep learning of many-body observables and quantum information scrambling Quantum (2024) doi:10.22331/q-2024-07-18-1417
- Zapletal, P, McMahon, N, Hartmann, M. Error-tolerant quantum convolutional neural networks for symmetry-protected topological phases Physical Review Research (2024) doi:10.1103/PhysRevResearch.6.033111
- Schmid, M, Braun, S, Sollacher, R, Hartmann, M. Highly efficient encoding for job-shop scheduling problems and its application on quantum computers Quantum Science and Technology (2024) doi:10.1088/2058-9565/ad9cba
- Eckstein, T, Mansuroglu, R, Czarnik, P, Zhu, J, et al. Large-scale simulations of Floquet physics on near-term quantum computers npj Quantum Information (2024) doi:10.1038/s41534-024-00866-1
- Mansuroglu, R, Adil, A, Hartmann, M, Holmes, Z, et al. Quantum Tensor-Product Decomposition from Choi-State Tomography PRX Quantum (2024) doi:10.1103/PRXQuantum.5.030306
- Mansuroglu, R, Fischer, F, Hartmann, M. Problem-specific classical optimization of Hamiltonian simulation Physical Review Research (2023) doi:10.1103/PhysRevResearch.5.043035
- Meyer, N, Scherer, D, Plinge, A, Mutschler, C, et al. Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning (2023) doi:10.1109/QCE57702.2023.10181
- Meyer, N, Scherer, D, Plinge, A, Mutschler, C, et al. Quantum Policy Gradient Algorithm with Optimized Action Decoding (2023)
- Heunisch, L, Eichler, C, Hartmann, M. Tunable coupler to fully decouple and maximally localize superconducting qubits Physical Review Applied (2023) doi:10.1103/PhysRevApplied.20.064037
- Mansuroglu, R, Eckstein, T, Nützel, L, Wilkinson, S, et al. Variational Hamiltonian simulation for translational invariant systems via classical pre-processing Quantum Science and Technology (2023) doi:10.1088/2058-9565/acb1d0
- Pechal, M, Roy, F, Wilkinson, S, Salis, G, et al. Direct implementation of a perceptron in superconducting circuit quantum hardware Physical Review Research (2022) doi:10.1103/PhysRevResearch.4.033190
- Herrmann, J, Llima, S, Remm, A, Zapletal, P, et al. Realizing quantum convolutional neural networks on a superconducting quantum processor to recognize quantum phases Nature Communications (2022) doi:10.1038/s41467-022-31679-5
- Baker, A, Huber, G, Glaser, N, Roy, F, et al. Single shot i-Toffoli gate in dispersively coupled superconducting qubits Applied Physics Letters (2022) doi:10.1063/5.0077443
- Feulner, V, Hartmann, M. Variational quantum eigensolver ansatz for the J1-J2 -model Physical Review B (2022) doi:10.1103/PhysRevB.106.144426
- Fischer, M, Chen, Q, Besson, C, Eder, P, et al. In situ tunable nonlinearity and competing signal paths in coupled superconducting resonators Physical Review B (2021) doi:10.1103/PhysRevB.103.094515
- Wilkinson, S, Hartmann, M. Superconducting quantum many-body circuits for quantum simulation and computing Applied Physics Letters (2020) doi:10.1063/5.0008202
- Duncan, C, Hartmann, M, Thomson, R, Öhberg, P. Synthetic mean-field interactions in photonic lattices European Physical Journal D (2020) doi:10.1140/epjd/e2020-100521-0
- Hartmann, M, Carleo, G. Neural-Network Approach to Dissipative Quantum Many-Body Dynamics Physical Review Letters (2019) doi:10.1103/PhysRevLett.122.250502
- Arute, F, Arya, K, Babbush, R, Bacon, D, et al. Quantum supremacy using a programmable superconducting processor Nature (2019) doi:10.1038/s41586-019-1666-5