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Ivan Rungger

Ivan Rungger

Principal scientist / Team lead

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 management board member of the UK Hub for Quantum Computing via Integrated and Interconnected Implementations (QCI3), where he co-leads the theme aiming to integrate quantum algorithms for quantum advantage with quantum computing hardware developed in the hub. He 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. 

Main Research Interests 

  1. Development of metrics and benchmarks for quantum computers for performance assurance, which includes the open source QCMet software package (https://qcmet.npl.co.uk)  

  1. Development of algorithms and software for classical and quantum computers in materials science, quantum chemistry and machine learning, which includes the pyTTN tensor network library (https://gitlab.npl.co.uk/qsm/pyttn)  

  1. Development of noise models and theoretical explanations and guidance for experiments  

  1. Development of trustworthy machine learning and AI methods for scalable quantum technologies 

Biography 

Ivan joined NPL in May 2015 to establish a theory group working on quantum and classical computing as well as on multi-scale device modelling. He obtained his PhD in computational physics from Trinity College Dublin in 2009, where he continued as a research fellow, and developed computational methods for electronic structure and quantum transport theory. His publication list includes more than 100 peer reviewed articles. 

Selected Recent 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  

  1. 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  

  1. 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  

  1. 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  

  1. 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  

  1. 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 

See Google Scholar for a full list of Ivan’s publications.