Quantum Many-Body Dynamics Research Unit

Principal Investigator

PI Name Takeshi Fukuhara
Degree D.Sci.
Title Unit Leader
Brief Resume
2009D. Sci., Kyoto University
2009Researcher, ERATO Ueda Macroscopic Quantum Control Project, Japan Science and Technology Agency
2010Postdoctoral researcher, Max Planck Institute of Quantum Optics, Germany
2014Unit Leader, Quantum Many-Body Dynamics Research Unit, RIKEN Center for Emergent Matter Science (-present)



Modern technology has been progressed based on understanding of quantum many-body systems. In addition to the conventional study of equilibrium states, non-equilibrium dynamics plays an important role in developing further intriguing materials and advancing quantum information processing technology. In this research unit, we investigate non-equilibrium dynamics of quantum many-body systems using ultracold atomic gases. Advantages of ultracold-atom experiments are simplicity and excellent controllability of the parameters, including dimensions, of the systems. Especially, a quantum gas loaded into periodic potential generated by a laser (optical lattice) can mimic fundamental models in the strongly correlated physics, and it can be used as a platform for quantum information processing. Utilizing such systems, we investigate real-time and real-space dynamics, and also control the many-body dynamics.

Research Fields

Physics, Engineering


Quantum simulation
Quantum computing
Cold atoms
Bose-Einstein condensation
Optical lattice


Automatic optimization of cold-atom experiments with machine learning

Cold atoms offer a promising platform for quantum simulation, quantum sensing and metrology. For such experiments, it is necessary to adjust various experimental parameters. If the parameter optimization can be conducted automatically, it will be possible to optimize a large number of parameters, which a human can hardly handle, and to discover an unknown new method. To this end, we employ machine-learning-based optimization, which will become a key technology for quantum information processing.

We have optimized evaporative cooling using Bayesian optimization. After 300 trials within 3 hours, Bayesian optimization discovered trajectories that exhibited performance comparable with those of manual tuning by a human expert. Interestingly, the obtained evaporation trajectories were significantly different from the manual one. Furthermore, by analyzing the machine-learned trajectories, we succeeded in extracting minimum requirements for successful evaporative cooling.

(a) Optimized trajectories of evaporative cooling. The color of the lines represents the score obtained with a corresponding experiment. The gray dashed line shows the trajectory we manually optimized. (b) Correlation plots between powers of two FORT beams.


  1. F. Schäfer, T. Fukuhara, S. Sugawa, Y. Takasu, and Y. Takahashi,

    Tools for quantum simulation with ultracold atoms in optical lattices

    Nat. Rev. Phys. 2, 411 (2020)
  2. D. Yamamoto, T. Fukuhara, and I. Danshita

    Frustrated quantum magnetism with Bose gases in triangular optical lattices at negative absolute temperatures

    Commun. Phys. 3, 56 (2020)
  3. I. Nakamura, A. Kanemura, T. Nakaso, R. Yamamoto, and T. Fukuhara

    Non-standard trajectories found by machine learning for evaporative cooling of Rb87 atoms

    Opt. Express 27, 20435 (2019)
  4. N. T. Phuc, T. Momoi, S. Furukawa, Y. Kawaguchi, T. Fukuhara, and M. Ueda

    Geometrically frustrated coarsening dynamics in spinor Bose-Fermi mixtures

    Phys. Rev. A 95, 013620 (2017)
  5. T. Fukuhara, S. Hild, J. Zeiher, P. Schauss, I. Bloch, M. Endres, and C. Gross

    Spatially Resolved Detection of a Spin-Entanglement Wave in a Bose-Hubbard Chain

    Phys. Rev. Lett. 115, 035302 (2015)


  • Sep 25, 2015 RIKEN RESEARCH Entangled atoms
    The observation of quantum entangled atoms has important implications for quantum information processing