Machine Learning for Quantum Technology
About
The research of our Helmholtz Young Investigator Group is directed at understanding and controlling the collective dynamics of quantum many-body systems far from equilibrium, which may ultimately pave the way to devise new technologies that rely on the quantum laws of nature. However, the theoretical approach is rather demanding: For investigations based on microscopic model systems one has to devise strategies to deal with the curse of dimensionality inherent to the quantum many-body problem. Particularly challenging is the development of efficient and versatile computational methods that serve as crucial link between experimental observations and theoretical models. An alternative route is to use a quantum computer for the simulation, which – in turn – means to realize a highly controlled quantum-dynamical process. To make progress in this field, our group brings in ideas from modern machine learning, where quantum challenges match their natural strengths and, reversely, the quantum applications call for the development of new machine learning techniques.
Research Topics
- Neural quantum states
- Reinforcement learning for quantum control and interactive quantum dynamics
- Algorithms for digital quantum simulation
- Non-equilibrium quantum many-body physics
Members
More about our research
Enormous advances in experimental techniques during the past two decades, like the development of quantum simulators of different kinds and ultrafast pump-probe techniques, allow unprecedented control and time-resolved observations of quantum systems with many interacting degrees of freedom.
These developments turned the spotlight on a number of open theoretical questions: Under which conditions and in what sense will a closed system approach a thermal state when prepared far from equilibrium initially? How can the dynamics be characterized on a macroscopic level and what are characteristic time scales? Is there a notion of phases beyond the equilibrium paradigm? We develop and employ computational approaches in order to find answers to such fundamental questions.
An example is the exploration of fluctuations occurring in non-equilibrium processes implemented on a quantum processor: “Quantum Many-Body Jarzynski Equality and Dissipative Noise on a Digital Quantum Computer”, Hahn et al., Phys. Rev. X 13, 041023 (2023).
Projects
Helmholtz Young Investigator Group “Machine Learning for Quantum Technology” (09/2022-08/2027)
Selected publications
Roughening dynamics of interfaces in two-dimensional quantum matter
W. Krinitsin, N. Tausendpfund, M. Rizzi, M. Heyl, M. Schmitt
arXiv:2412.10145
Wave function network description and Kolmogorov complexity of quantum many-body systems
T. Mendes-Santos, M. Schmitt, A. Angelone, A. Rodriguez, P. Scholl, H. J. Williams, D. Barredo, T. Lahaye, A. Browaeys, M. Heyl, and M. Dalmonte
Phys. Rev. X 14, 021029 (2024)
Reinforcement learning pulses for transmon qubit entangling gates
H.N. Nguyen, F. Motzoi, M. Metcalf, K.B. Whaley, M. Bukov, M. Schmitt
Mach. Learn.: Science and Technology 5, 025066 (2024)
Quantum Many-Body Jarzynski Equality and Dissipative Noise on a Digital Quantum Computer,
Dominik Hahn, Maxime Dupont, Markus Schmitt, David J. Luitz, Marin Bukov,
Phys. Rev. X 13, 041023 (2023)
Quantum phase transition dynamics in the two-dimensional transverse-field Ising model
M. Schmitt, M. M. Rams, J. Dziarmaga, M. Heyl, W. H. Zurek
Sci. Adv. 8, abl6850 (2022)
Quantum many-body dynamics in two dimensions with artificial neural networks
M. Schmitt and M. Heyl
Phys. Rev. Lett. 125, 100503 (2020)