Dr Jack Araz
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About
Overview
Dr Araz is Director of the Quantum Science and Technology Research Group, where his research brings together quantum computing, artificial intelligence and fundamental physics. His work addresses a central challenge in modern science: how to make reliable, uncertainty-aware predictions and draw robust conclusions from complex physical systems, data and simulations that push the limits of conventional computation.
His research combines two closely connected areas. The first develops quantum algorithms and simulation methods to calculate physical phenomena that are challenging for classical computers. The second develops statistical and AI-based methods that quantify uncertainty and improve the reliability of data-driven scientific inference. Together, these approaches aim to develop new computational tools for fundamental physics and establish trustworthy methods for extracting knowledge from complex scientific data.
Dr Araz is also a lead developer of widely used particle-physics software, including Spey and MadAnalysis 5, and contributes to the Rivet and Contur projects. He plays an active role in the international collider-physics community through the CERN Reinterpretation Working Group, helping to advance the preservation, reinterpretation and reuse of experimental results. Alongside his research, he is committed to training and supporting the next generation of scientists through teaching, mentoring and international outreach programmes.
Publications
Publications by category
Chapters (2)
- Araz, J.Y. (2021). Correction to: Leptophobic Z′ in Supersymmetry and Where to Find Them. Quantum Theory and Symmetries (pp. C1-C3). Springer International Publishing. ISBN 9783030557768.
- Araz, J.Y. (2021). Leptophobic Z′ in Supersymmetry and Where to Find Them. Quantum Theory and Symmetries (pp. 559-568). Springer International Publishing. ISBN 9783030557768.
Journal articles (32)
- Araz, J.Y., Englert, C., Kirk, M. and Tetlalmatzi-Xolocotzi, G. (2026). New insights into the $$b\rightarrow c \bar{u}q$$ puzzle through Top–Bottom synergies. The European Physical Journal C, 86(9). doi:10.1140/epjc/s10052-026-16297-5
- Araz, J.Y., Beck, A., Reboud, M., Spannowsky, M. and van Dyk, D. (2026). Communicating likelihoods with normalising flows. The European Physical Journal C, 86(7). doi:10.1140/epjc/s10052-026-16045-9
- Araz, J.Y. and Spannowsky, M. (2026). The role of data embedding in quantum autoencoders for improved anomaly detection. Quantum Machine Intelligence, 8(1). doi:10.1007/s42484-026-00404-6
- Araz, J.Y., Fuks, B., Goodsell, M.D. and Murphy, T. (2026). Deciphering compressed electroweakino excesses with MadAnalysis5. The European Physical Journal C, 86(5). doi:10.1140/epjc/s10052-026-15710-3
- Araz, J.Y., Athanasakos, D., Ploskon, M. and Ringer, F. (2026). Graph theory inspired anomaly detection at the LHC. Journal of High Energy Physics, 2026(2). doi:10.1007/jhep02(2026)254
- Araz, J.Y., Mikuni, V., Ringer, F., Sato, N., Torales Acosta, F. and Whitehill, R. (2025). Point cloud-based diffusion models for the Electron-Ion Collider. Physics Letters B, 868, pp. 139694-139694. doi:10.1016/j.physletb.2025.139694
- Araz, J.Y., Grau, M., Montgomery, J. and Ringer, F. (2025). Hybrid quantum simulations with qubits and qumodes on trapped-ion platforms. Physical Review A, 112(1). doi:10.1103/kbv4-jj51
- Araz, J.Y., Bhowmick, S., Grau, M., McEntire, T.J. and Ringer, F. (2025). State preparation of lattice field theories using quantum optimal control. Physical Review D, 111(3). doi:10.1103/physrevd.111.034506
- Araz, J.Y., Buckley, A., Kasieczka, G., Kieseler, J., Kraml, S., Kvellestad, A.... Ünel, G. (2024). Les Houches guide to reusable ML models in LHC analyses. SciPost Physics Community Reports. doi:10.21468/scipostphyscommrep.3
- Araz, J.Y., Spannowsky, M. and Wingate, M. (2024). Exploring thermal equilibria of the Fermi-Hubbard model with variational quantum algorithms. Physical Review A, 109(6). doi:10.1103/physreva.109.062422
- Araz, J.Y. (2024). Spey: Smooth inference for reinterpretation studies. SciPost Physics, 16(1). doi:10.21468/scipostphys.16.1.032
- Araz, J.Y. and Spannowsky, M. (2023). Quantum-probabilistic Hamiltonian learning for generative modeling and anomaly detection. Physical Review A, 108(6). doi:10.1103/physreva.108.062422
- Araz, J.Y., Buckley, A. and Fuks, B. (2023). Searches for new physics with boosted top quarks in the MadAnalysis 5 and Rivet frameworks. The European Physical Journal C, 83(7). doi:10.1140/epjc/s10052-023-11779-2
- Araz, J.Y., Buckley, A., Fuks, B., Reyes-González, H., Waltenberger, W., Williamson, S.L.... Yellen, J. (2023). Strength in numbers: Optimal and scalable combination of LHC new-physics searches. SciPost Physics, 14(4). doi:10.21468/scipostphys.14.4.077
- Araz, J.Y., Schenk, S. and Spannowsky, M. (2023). Toward a quantum simulation of nonlinear sigma models with a topological term. Physical Review A, 107(3). doi:10.1103/physreva.107.032619
- Alguero, G., Araz, J., Fuks, B. and Kraml, S. (2023). Signal region combination with full and simplified likelihoods in MadAnalysis 5. SciPost Physics, 14(1). doi:10.21468/scipostphys.14.1.009
- Araz, J.Y. and Spannowsky, M. (2022). Classical versus quantum: Comparing tensor-network-based quantum circuits on Large Hadron Collider data. Physical Review A, 106(6). doi:10.1103/physreva.106.062423
- Araz, J.Y., Criado, J.C. and Spannowsky, M. (2022). Identifying magnetic antiskyrmions while they form with convolutional neural networks. Journal of Magnetism and Magnetic Materials, 563, pp. 169806-169806. doi:10.1016/j.jmmm.2022.169806
- Araz, J.Y., Fuks, B., Goodsell, M.D. and Utsch, M. (2022). Recasting LHC searches for long-lived particles with MadAnalysis 5. The European Physical Journal C, 82(7). doi:10.1140/epjc/s10052-022-10511-w
- Araz, J.Y., Frank, M., Fuks, B., Moretti, S. and Özdal, Ö. (2021). Cross-fertilising extra gauge boson searches at the LHC. Journal of High Energy Physics, 2021(11). doi:10.1007/jhep11(2021)014
- Araz, J.Y. and Spannowsky, M. (2021). Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States. Journal of High Energy Physics, 2021(8). doi:10.1007/jhep08(2021)112
- Araz, J.Y. and Spannowsky, M. (2021). Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks. Journal of High Energy Physics, 2021(4). doi:10.1007/jhep04(2021)296
- Araz, J.Y., Fuks, B. and Polykratis, G. (2021). Simplified fast detector simulation in MadAnalysis 5. The European Physical Journal C, 81(4). doi:10.1140/epjc/s10052-021-09052-5
- Araz, J.Y., Banerjee, S., Gupta, R.S. and Spannowsky, M. (2021). Precision SMEFT bounds from the VBF Higgs at high transverse momentum. Journal of High Energy Physics, 2021(4). doi:10.1007/jhep04(2021)125
- Araz, J.Y. and Fuks, B. (2021). Implementation of the ATLAS-SUSY-2018-31 analysis in the MadAnalysis 5 framework (sbottoms with multi-bottoms and missing transverse energy; 139 fb−1). Modern Physics Letters A, 36(01), pp. 2141010-2141010. doi:10.1142/s0217732321410108
- Araz, J.Y., Conte, E., Ducrocq, R., Flacke, T., Fuks, B., Jeon, S.H.... Sengupta, D. (2021). Proceedings of the second MadAnalysis 5 workshop on LHC recasting in Korea. Modern Physics Letters A, 36(01), pp. 2102001-2102001. doi:10.1142/s0217732321020016
- Araz, J.Y. and Fuks, B. (2021). Implementation of the ATLAS-SUSY-2018-32 analysis (sleptons and electroweakinos with two leptons and missing transverse energy; 139 fb−1). Modern Physics Letters A, 36(01), pp. 2141005-2141005. doi:10.1142/s0217732321410054
- Abdallah, W., Salam, S.A., Ahmadov, A., Ahriche, A., Alguero, G., Allanach, B.... Zurita, J. (2020). Reinterpretation of LHC Results for New Physics: Status and recommendations after Run 2. SciPost Physics, 9(2). doi:10.21468/scipostphys.9.2.022
- Araz, J.Y., Frank, M. and Fuks, B. (2020). Reinterpreting the results of the LHC with MadAnalysis 5: uncertainties and higher-luminosity estimates. The European Physical Journal C, 80(6). doi:10.1140/epjc/s10052-020-8076-6
- Araz, J.Y., Banerjee, S., Frank, M., Fuks, B. and Goudelis, A. (2018). Dark matter and collider signals in an MSSM extension with vector-like multiplets. Physical Review D, 98(11). doi:10.1103/physrevd.98.115009
- Araz, J.Y., Corcella, G., Frank, M. and Fuks, B. (2018). Loopholes in Z′ searches at the LHC: exploring supersymmetric and leptophobic scenarios. Journal of High Energy Physics, 2018(2). doi:10.1007/jhep02(2018)092
- Araz, J.Y., Frank, M. and Fuks, B. (2017). DifferentiatingU(1)′supersymmetric models with right sneutrino and neutralino dark matter. Physical Review D, 96(1). doi:10.1103/physrevd.96.015017
Professional activities
Keynote lecture/speech
- Beyond 5σ: Teaching AI to Know What It Doesn’t Know. University of Edinburgh (2026). IOP Joint APP and HEPP Annual Conference 2026