Ivan Rungger
Principal Scientist at NPL, Professor at Royal Holloway
Research Interests:
algorithms for materials science and chemistry simulations; noise models for quantum technologies; metrics and benchmarks for quantum computers; machine learning and AI; tensor networks; quantum networks
Ivan Rungger is a Principal Scientist at the National Physical Laboratory (NPL), where he leads the Quantum Software and Modelling (QSM) team, and Professor in Computer Science at Royal Holloway University of London on a join appointment with NPL. He has developed classical and quantum computing algorithms and software for materials science and chemistry simulations, including solvers for the Hubbard model and open quantum systems using embedding methods such as the dynamical mean field theory (DMFT). A core research direction is the development of metrics and benchmarks for quantum computers, and the modelling of noise in hardware. Ivan is actively involved in the international standardization activities for quantum computing in support of the UK’s quantum technologies industry, where NPL plays a central role. The current research of the team centres around four core themes: 1. Development of metrics and benchmarks for quantum computers, which includes the open source QCMet software package (https://qcmet.npl.co.uk) 2. Development of algorithms and software for classical and quantum computers in materials science and quantum chemistry, which includes the pyTTN tensor network library (https://gitlab.npl.co.uk/qsm/pyttn) 3. Development of noise models and theoretical explanations and guidance for experiments 4. Development of trustworthy machine learning methods for quantum technologies
Featured Publications 1. F. Jamet, L. P. Lindoy, Y. Rath, C. P. Lenihan, A. Agarwal, E. Fontana, F. Simkovic, B. A. Martin, I. Rungger, Anderson impurity solver integrating tensor network methods with quantum computing. APL Quantum 2, 016121 (2025); https://doi.org/10.1063/5.0245488 2. A. Agarwal, L. P. Lindoy, D. Lall, F. Jamet, I. Rungger, Modelling non-Markovian noise in driven superconducting qubits. Quant. Sci. Technol. 9, 035017 (2024); https://doi.org/10.1088/2058-9565/ad3d7e 3. D. Lall, A. Agarwal, W. Zhang, L. P. Lindoy, T. Lindström, S. Webster, S. Hall, N. Chancellor, P. Wallden, R. Garcia-Patron, E. Kashefi, V. Kendon, J. Pritchard, A. Rossi, A. Datta, T. Kapourniotis, K. Georgopoulos, I. Rungger, A Review and Collection of Metrics and Benchmarks for Quantum Computers: definitions, methodologies and software, arXiv:2502.06717 (2025); https://arxiv.org/abs/2502.06717 4. L. P. Lindoy, D. Rodrigo Albert, Y. Rath, I. Rungger, pyTTN: An Open Source Toolbox for Open and Closed System Quantum Dynamics Simulations Using Tree Tensor Networks. arXiv:2503.15460 (2025); https://arxiv.org/abs/2503.15460 5. C. Lupo, F. Jamet, W. Tse, I. Rungger, C. Weber, Maximally localized dynamical quantum embedding for solving many-body correlated systems. Nature Comp. Sci. 1, 502 (2021); https://doi.org/10.1038/s43588-021-00105-z 6. J. Tilly, H. Chen, S. Cao, D. Picozzi, K. Setia, Y. Li, E., L. Wossnig, I. Rungger, G. Booth, J. Tennyson, The Variational Quantum Eigensolver: A review of methods and best practices. Physics Reports. 986, 1 (2022); https://doi.org/10.1016/j.physrep.2022.08.003