Hartmann Group

Below are the publications indexed by the CRIS.

You may also consult the pre-prints on the arXiv.

  1. 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
  2. Marti, L, Mansuroglu, R, Hartmann, M. Efficient Quantum Cooling Algorithm for Fermionic Systems Quantum (2025) doi:10.22331/q-2025-02-18-1635
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. Mansuroglu, R, Fischer, F, Hartmann, M. Problem-specific classical optimization of Hamiltonian simulation Physical Review Research (2023) doi:10.1103/PhysRevResearch.5.043035
  10. 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
  11. Meyer, N, Scherer, D, Plinge, A, Mutschler, C, et al. Quantum Policy Gradient Algorithm with Optimized Action Decoding (2023)
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. Feulner, V, Hartmann, M. Variational quantum eigensolver ansatz for the J1-J2 -model Physical Review B (2022) doi:10.1103/PhysRevB.106.144426
  18. 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
  19. Wilkinson, S, Hartmann, M. Superconducting quantum many-body circuits for quantum simulation and computing Applied Physics Letters (2020) doi:10.1063/5.0008202
  20. 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
  21. Hartmann, M, Carleo, G. Neural-Network Approach to Dissipative Quantum Many-Body Dynamics Physical Review Letters (2019) doi:10.1103/PhysRevLett.122.250502
  22. 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