Priya Batra

Priya Batra

PhD Student at Department of Physics, Indian Institute of Science Education Research Pune
Prime Minister Research Fellow

Machine Learning meets Quantum Physics

Quantum many body system

  1. Carrasquilla, J. and Melko, R.G., 2017. Machine learning phases of matter. Nature Physics, 13(5), pp.431-434.
  2. Ch’Ng, K., Carrasquilla, J., Melko, R.G. and Khatami, E., 2017. Machine learning phases of strongly correlated fermions. Physical Review X, 7(3), p.031038.
  3. Broecker, P., Carrasquilla, J., Melko, R.G. and Trebst, S., 2017. Machine learning quantum phases of matter beyond the fermion sign problem. Scientific reports, 7(1), pp.1-10.
  4. Torlai, G., Mazzola, G., Carrasquilla, J., Troyer, M., Melko, R. and Carleo, G., 2018. Neural-network quantum state tomography. Nature Physics, 14(5), pp.447-450.
  5. Melko, R.G., Carleo, G., Carrasquilla, J. and Cirac, J.I., 2019. Restricted Boltzmann machines in quantum physics. Nature Physics, 15(9), pp.887-892.

Reinforcement learning for quantum physics

  1. Lin, J., Lai, Z.Y. and Li, X., 2018. Reinforcement-learning-based architecture for automated quantum adiabatic algorithm design. arXiv, pp.arXiv-1812.
  2. Bukov, M., 2018. Reinforcement learning for autonomous preparation of Floquet-engineered states: Inverting the quantum Kapitza oscillator. Physical Review B, 98(22), p.224305.
  3. Bukov, M., Day, A.G., Sels, D., Weinberg, P., Polkovnikov, A. and Mehta, P., 2018. Reinforcement learning in different phases of quantum control. Physical Review X, 8(3), p.031086.
  4. Fösel, T., Tighineanu, P., Weiss, T. and Marquardt, F., 2018. Reinforcement learning with neural networks for quantum feedback. Physical Review X, 8(3), p.031084.
  5. Niu, M.Y., Boixo, S., Smelyanskiy, V.N. and Neven, H., 2019. Universal quantum control through deep reinforcement learning. npj Quantum Information, 5(1), pp.1-8.
  6. Zhang, X.M., Wei, Z., Asad, R., Yang, X.C. and Wang, X., 2019. When reinforcement learning stands out in quantum control? A comparative study on state preparation. arXiv preprint arXiv:1902.02157.
  7. An, Z. and Zhou, D.L., 2019. Deep reinforcement learning for quantum gate control. EPL (Europhysics Letters), 126(6), p.60002.
  8. Alam, M.S., 2019. Quantum Logic Gate Synthesis as a Markov Decision Process. arXiv preprint arXiv:1912.12002.
  9. Albarrán-Arriagada, F., Retamal, J.C., Solano, E. and Lamata, L., 2020. Reinforcement learning for semi-autonomous approximate quantum eigensolver. Machine Learning: Science and Technology, 1(1), p.015002.