Skip to content
Menu
Close
Sign up for NPL updates
Close
Sign up for NPL updates

For people, place, prosperity and planet, we deliver impact with measurement science

Deep Lall

Deep Lall

PhD student [UoS]

Deep Lall is a PhD student at the University of Strathclyde but is based at NPL in the Quantum Software and Modelling team within the Quantum Technologies department. His research is focused on leveraging noise as a resource in quantum algorithms for materials simulation. He also develops software tools to support the development and benchmarking of quantum hardware and software. His research focuses on how best to benchmark quantum computers, including the design of scalable and efficient benchmarking protocols. He is also a member of international standards bodies and working groups dedicated to establishing global standards for quantum computing, with a particular emphasis on benchmarking.  

Main Research Interests 

  • Benchmarking quantum computers, characterising noise in quantum computers, quantum algorithms for materials simulation, leveraging noise as a resource in quantum algorithms. 

Software 

Biography 

Deep received his MSci in Physics from Imperial College London in 2017. He worked in industry for 2 years until he decided to return to academia and undertook an MSc in Quantum Technologies from University College London in 2020. He then joined NPL as a research scientist in the Quantum Software and Modelling team under Ivan Rungger. 

Selected Publications 

  • 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   

  • S. Cao , D. Lall, M. Bakr, G. Campanaro, S. D. Fasciati, J. Wills, V. Chidambaram, B. Shteynas, I Rungger, P. J. Leek, Efficient characterization of qudit logical gates with gate set tomography using an error-free virtual Z gate model.  Phys. Rev. Lett. 133(12), 120802. 

  • 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   

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

Email Deep Lall