Evert van Nieuwenburg
Assistant Professor
- Name
- Dr. E.P.L. van Nieuwenburg
- Telephone
- +31 71 527 5523
- e.p.l.van.nieuwenburg@liacs.leidenuniv.nl
- ORCID iD
- 0000-0003-0323-0031
Evert is a computational physicist who focuses his attention on various uses of machine learning and artificial intelligence to physics. That ranges from detecting phase transitions all the way to the automated and intelligent control of state of the art (quantum) experiments. He also studies quantum algorithms and variational quantum circuits under the theme of quantum AI and quantum games, and developed Quantum TiqTaqToe.
More information about Evert van Nieuwenburg
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Evert is a computational physicist who focuses his attention on various uses of machine learning and artificial intelligence to physics. That ranges from detecting phase transitions all the way to the automated and intelligent control of state of the art (quantum) experiments. He also studies quantum algorithms and variational quantum circuits under the theme of quantum AI and quantum games, and developed Quantum TiqTaqToe.
Assistant Professor
- Science
- Leiden Inst of Advanced Computer Science
- Benestad J., Krzywda J.A., Nieuwenburg E.P.L. van & Danon J. (2024), Efficient adaptive Bayesian estimation of a slowly fluctuating Overhauser field gradient, SciPost Physics 17: 014.
- Benestad J., Tsintzis A., Seoane Souto R., Leijnse M., Nieuwenburg E. van & Danon J. (2024), Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements, Physical Review B 110: 075402.
- Berritta F., Krzywda J.A., Benestad J., van der Heijden J., FedeleF., FallahiS., Gardner GC., Manfra M.J., Nieuwenburg E.P.L. van., Danon J., Chatterjee A. & Kuemmeth F. (2024), Physics-informed tracking of qubit fluctuations, Physical Review Applied 22: 014033.
- Berritta F., Rasmussen T., Krzywda J.A., van der Heijden J., Fedele F., Nieuwenburg E.P.L. van & et al (2024), Real-time two-axis control of a spin qubit, Nature Communications 15: 1676.
- Meinerz K., Trebst S., Rudner M. & Nieuwenburg E.P.L. van (2024), The quantum cartpole: a benchmark environment for non-linear reinforcement learning, SciPost Physics 7(2): 026.
- Wauters M.M. & Nieuwenburg E. van (2022), Reusability report: comparing gradient descent and Monte Carlo tree search optimization of quantum annealing schedules, Nature Machine Intelligence 4(10): 810-813.
- Ontwikkelen educatieve programma's en outreach rond quantum games