Franklin, A. Experiment, Right or Wrong (Cambridge Univ. Press, 1990).
Radder, H. The Philosophy of Scientific Experimentation (Univ. Pittsburgh Press, 2003).
Shamos, M. H. Great Experiments in Physics: Firsthand Accounts from Galileo to Einstein (Courier Corporation, 1987).
Krenn, M., Malik, M., Fickler, R., Lapkiewicz, R. & Zeilinger, A. Automated search for new quantum experiments. Phys. Rev. Lett. 116, 090405 (2016). This paper presents Melvin, an early AI-driven framework for the design of photonic quantum information experiments, with several of its proposed experimental configurations subsequently realized in laboratories.
Knott, P. A search algorithm for quantum state engineering and metrology. New J. Phys. 18, 073033 (2016). This paper presents Tachikoma, an early AI-driven framework for designing quantum metrology experiments, initially using genetic algorithms and later incorporating neural network surrogate models.
Ruiz-Gonzalez, C. et al. Digital discovery of 100 diverse quantum experiments with PyTheus. Quantum 7, 1204 (2023). This paper presents PyTheus, a discovery framework for photonic quantum optics that enables efficient exploration of experimental designs via an overcomplete, physics-inspired continuous search space, yielding 100 diverse quantum experiments.
Fürrutter, F., Muñoz-Gil, G. & Briegel, H. J. Quantum circuit synthesis with diffusion models. Nat. Mach. Intell. 6, 515–524 (2024).
Landgraf, J., Wanjura, C. C., Peano, V. & Marquardt, F. Artificial discovery of lattice models for wave transport. Preprint at https://doi.org/10.48550/arXiv.2508.10693 (2025).
Babazadeh, A. et al. High-dimensional single-photon quantum gates: concepts and experiments. Phys. Rev. Lett. 119, 180510 (2017).
Erhard, M., Malik, M., Krenn, M. & Zeilinger, A. Experimental Greenberger–Horne–Zeilinger entanglement beyond qubits. Nat. Photonics 12, 759–764 (2018).
Qian, K. et al. Multiphoton non-local quantum interference controlled by an undetected photon. Nat. Commun. 14, 1480 (2023).
Krenn, M., Hochrainer, A., Lahiri, M. & Zeilinger, A. Entanglement by path identity. Phys. Rev. Lett. 118, 080401 (2017).
Wang, K. et al. Entangling independent particles by path identity. Phys. Rev. Lett. 133, 233601 (2024).
Arlt, S., Ruiz-Gonzalez, C. & Krenn, M. Emulating multiparticle emitters with pair-sources: digital discovery of a quantum optics building block. Quantum Sci. Technol. 10, 015042 (2024).
Paul, E., Landreman, M., Bader, A. & Dorland, W. An adjoint method for gradient-based optimization of stellarator coil shapes. Nucl. Fusion 58, 076015 (2018).
Goodman, A. G. et al. Quasi-isodynamic stellarators with low turbulence as fusion reactor candidates. PRX Energy 3, 023010 (2024).
Baranov, A. et al. Optimising the active muon shield for the SHiP experiment at CERN. J. Phys. Conf. Ser. 934, 012050 (2017).
Cisbani, E. et al. AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case. J. Instrum. 15, P05009 (2020). This paper demonstrates the use of Bayesian optimization to improve the design of a large-scale particle physics detector within a fixed topology search space.
Dorigo, T. et al. Toward the end-to-end optimization of particle physics instruments with differentiable programming. Rev. Phys. 10, 100085 (2023).
Rolla, J. et al. Using evolutionary algorithms to design antennas with greater sensitivity to ultrahigh energy neutrinos. Phys. Rev. D 108, 102002 (2023). This paper demonstrates AI-driven design of highly sensitive ultrahigh energy neutrino antennas using evolutionary algorithms within a fixed topology search space.
Haack, C. et al. Machine-learning aided detector optimization of the Pacific Ocean Neutrino Experiment. In Proc. 38th International Cosmic Ray Conference (ICRC2023) id.1059 (2024).
Dorigo, T. et al. Toward the end-to-end optimization of the SWGO array layout. Nucl. Phys. B 1017, 116934 (2025).
Krenn, M., Drori, Y. & Adhikari, R. X. Digital discovery of interferometric gravitational wave detectors. Phys. Rev. X 15, 021012 (2025). This paper demonstrates that AI-driven exploration of overcomplete interferometric topology search spaces can discover novel gravitational wave detector topologies that outperform current next-generation designs under realistic conditions.
Nehme, E. et al. DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning. Nat. Methods 17, 734–740 (2020).
Rodríguez, C., Arlt, S., Möckl, L. & Krenn, M. Automated discovery of experimental designs in super-resolution microscopy with XLuminA. Nat. Commun. 15, 10658 (2024).
Huang, P.-S., Boyken, S. E. & Baker, D. The coming of age of de novo protein design. Nature 537, 320–327 (2016).
Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360–365 (2018).
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).
Sigmund, O. & Maute, K. Topology optimization approaches: a comparative review. Struct. Multidiscip. Optim. 48, 1031–1055 (2013).
Duris, J. et al. Bayesian optimization of a free-electron laser. Phys. Rev. Lett. 124, 124801 (2020).
Vendeiro, Z. et al. Machine-learning-accelerated Bose-Einstein condensation. Phys. Rev. Res. 4, 043216 (2022).
Degrave, J. et al. Magnetic control of tokamak plasmas through deep reinforcement learning. Nature 602, 414–419 (2022).
Reinschmidt, M., Fortágh, J., Günther, A. & Volchkov, V. V. Reinforcement learning in cold atom experiments. Nat. Commun. 15, 8532 (2024).
Buchli, J. et al. Improving cosmological reach of a gravitational wave observatory using Deep Loop Shaping. Science 389, 1012–1015 (2025).
Rainforth, T., Foster, A., Ivanova, D. R. & Bickford Smith, F. Modern Bayesian experimental design. Stat. Sci. 39, 100–114 (2024).
DeZoort, G., Battaglia, P. W., Biscarat, C. & Vlimant, J.-R. Graph neural networks at the Large Hadron Collider. Nat. Rev. Phys. 5, 281–303 (2023).
Dax, M. et al. Real-time inference for binary neutron star mergers using machine learning. Nature 639, 49–53 (2025).
Rajabzadeh, T., Boulton-McKeehan, A., Bonkowsky, S., Schuster, D. I. & Safavi-Naeini, A. H. A general framework for gradient-based optimization of superconducting quantum circuits using qubit discovery as a case study. Preprint at https://doi.org/10.48550/arXiv.2408.12704 (2024).
MacLellan, B. et al. Inverse design of photonic systems. Laser Photonics Rev. 18, 2300500 (2024).
Crafts, J., Fatemi, R., Horn, T. & Kalinkin, D. Performance optimization for a scintillating glass electromagnetic calorimeter at the EIC. J. Instrum. 19, C05049 (2024).
Wang, J., Bacharach, L., Larzabal, P. & El Korso, M. N. A comparison of antenna placement criteria based on the Cramér–Rao and Barankin bounds for radio interferometer arrays. Signal Process. 219, 109404 (2024).
Miao, H., Yang, H., Adhikari, R. X. & Chen, Y. Quantum limits of interferometer topologies for gravitational radiation detection. Class. Quantum Gravity 31, 165010 (2014).
Shirobokov, S., Belavin, V., Kagan, M., Ustyuzhanin, A. & Baydin, A. G. Black-box optimization with local generative surrogates. Adv. Neural Inf. Process. Syst. 33, 14650–14662 (2020). This paper introduces a trust-region-constrained generative surrogate model as a novel optimization method for design tasks with black-box stochastic simulators.
Fanelli, C. et al. AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider. Nucl. Instrum. Methods Phys. Res. A 1047, 167748 (2023).
Schillings, P. & Erdmann, J. Fighting Newtonian noise with gradient-based optimization at the Einstein Telescope. Class. Quantum Gravity 42, 065025 (2025).
Badaracco, F., Harms, J. & Rei, L. Joint optimization of seismometer arrays for the cancellation of Newtonian noise from seismic body waves in the Einstein telescope. Class. Quantum Gravity 41, 025013 (2024).
Kagan, M. & Heinrich, L. Branches of a tree: taking derivatives of programs with discrete and branching randomness in high energy physics. Preprint at https://doi.org/10.48550/arXiv.2308.16680 (2023).
Hughes, T. W., Minkov, M., Williamson, I. A. & Fan, S. Adjoint method and inverse design for nonlinear nanophotonic devices. ACS Photonics 5, 4781–4787 (2018).
Sapra, N. V. On-chip integrated laser-driven particle accelerator. Science 367, 79–83 (2020). This paper demonstrates the AI-driven discovery of an on-chip integrated laser-driven particle accelerator by exploring a parametric topology search space encoded as a permittivity distribution.
Huang, S.-Y. et al. Multiphoton quantum interference at ultracompact inverse-designed multiport beam splitter. Opt. Quantum 3, 576–582 (2025).
Menke, T. et al. Automated design of superconducting circuits and its application to 4-local couplers. npj Quantum Inf. 7, 49 (2021). This paper demonstrates AI-driven discovery of novel superconducting quantum circuits through automated search over a slot-based topology search space.
Qasim, S. R., Owen, P. & Serra, N. Physics instrument design with reinforcement learning. Mach. Learn. Sci. Technol. 6, 035033 (2025).
Cervera-Lierta, A., Krenn, M. & Aspuru-Guzik, A. Design of quantum optical experiments with logic artificial intelligence. Quantum 6, 836 (2022).
Liu, H., Simonyan, K. & Yang, Y. Darts: differentiable architecture search. In Proc. 7th International Conference on Learning Representations (ICLR 2019) (2019).
Zhang, S.-X., Hsieh, C.-Y., Zhang, S. & Yao, H. Differentiable quantum architecture search. Quantum Sci. Technol. 7, 045023 (2022).
Sim, S., Johnson, P. D. & Aspuru-Guzik, A. Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Adv. Quantum Technol. 2, 1900070 (2019). This paper proposes practical metrics for evaluating the expressibility of parameterized quantum circuits, informing the design of overcomplete topology search spaces.
Olle, J., Yevtushenko, O. M. & Marquardt, F. Scaling the automated discovery of quantum circuits via reinforcement learning with gadgets. Preprint at https://doi.org/10.48550/arXiv.2503.11638 (2025).
Mirhoseini, A. et al. A graph placement methodology for fast chip design. Nature 594, 207–212 (2021).
Arlt, S. et al. Meta-designing quantum experiments with language models. Nat. Mach. Intell. 8, 148–157 (2026).
Wallnöfer, J., Melnikov, A. A., Dür, W. & Briegel, H. J. Machine learning for long-distance quantum communication. PRX Quantum 1, 010301 (2020).
Edelen, A. et al. Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems. Phys. Rev. Accel. Beams 23, 044601 (2020).
Kasim, M. F. et al. Building high accuracy emulators for scientific simulations with deep neural architecture search. Mach. Learn. Sci. Technol. 3, 015013 (2021).
Citrin, J. et al. Fast transport simulations with higher-fidelity surrogate models for ITER. Phys. Plasmas 30, 062501 (2023).
Ruiz-Gonzalez, C. et al. Neural surrogates for designing gravitational wave detectors. Preprint at https://doi.org/10.48550/arXiv.2511.19364 (2025).
Woźniak, K. A. et al. End-to-end optimal detector design with mutual information surrogates. Mach. Learn. Sci. Technol. 6, 045047 (2025). This paper demonstrates end-to-end particle physics detector design using a trust-region-constrained surrogate model and a general information-theoretic objective function.
Schmidt, K. et al. End-to-end detector optimization with diffusion models: a case study in sampling calorimeters. Particles 8, 47 (2025).
Martínez, M. P., Vidal, X. C. & Vischia, P. Automatic optimization of a parallel-plate avalanche counter with optical readout. Particles 8, 26 (2025).
Bein, S., Connor, P., Pedro, K., Schleper, P. & Wolf, M. Refining fast simulation using machine learning. In Proc. 26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023) 09032 (EDP Sciences, 2024).
Schuetz, A.-K., Poon, A. W. & Li, A. RESuM: a rare event surrogate model for physics detector design. In Proc. Thirteenth International Conference on Learning Representations (ICLR 2025) (2025).
Adelmann, A. et al. New directions for surrogate models and differentiable programming for High Energy Physics detector simulation. In Proc. 2021 U.S. Community Study on the Future of Particle Physics (2022).
Krause, C. et al. CaloChallenge 2022: a community challenge for fast calorimeter simulation. Rep. Prog. Phys. 88, 116201 (2025). This paper documents the CaloChallenge, a popular competition in the AI and particle physics communities aimed at developing diverse generative models to accelerate calorimeter simulation.
Bradbury, J. et al. JAX: composable transformations of Python+NumPy programs. GitHub https://github.com/jax-ml/jax (2018).
Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 8024–8035 (2019).
Abadi, M. et al. TensorFlow: a system for large-scale machine learning. In Proc. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) 265–283 (2016).
Kaiser, J., Xu, C., Eichler, A. & Santamaria Garcia, A. Bridging the gap between machine learning and particle accelerator physics with high-speed, differentiable simulations. Phys. Rev. Accel. Beams 27, 054601 (2024).
Citrin, J. et al. TORAX: a fast and differentiable tokamak transport simulator in JAX. Preprint at https://doi.org/10.48550/arXiv.2406.06718 (2024).
Klimesch, J., Drori, Y., Adhikari, R. X. & Krenn, M. Differometor: a differentiable interferometer simulator for the computational design of gravitational wave detectors. GitHub https://github.com/artificial-scientist-lab/Differometor (2025).
El-Kishky, A. et al. Competitive programming with large reasoning models. Preprint at https://doi.org/10.48550/arXiv.2502.06807 (2025).
Novikov, A. et al. AlphaEvolve: a coding agent for scientific and algorithmic discovery. Preprint at https://doi.org/10.48550/arXiv.2506.13131 (2025).
Moses, W. & Churavy, V. Instead of rewriting foreign code for machine learning, automatically synthesize fast gradients. Adv. Neural Inf. Process. Syst. 33, 12472–12485 (2020).
Sagebaum, M., Albring, T. & Gauger, N. R. High-performance derivative computations using CoDiPack. ACM Trans. Math. Softw. 45, 1–26 (2019).
Sagan, D., Hoffstaetter, G., Signorelli, M. & Coxe, A. Bmad-Julia: a Julia environment for accelerator simulations including machine learning. In Proc. 15th International Particle Accelerator Conference (IPAC’24) 2562–2565 (2024).
Wan, J., Alamprese, H., Ratcliff, C., Qiang, J. & Hao, Y. JuTrack: a Julia package for auto-differentiable accelerator modeling and particle tracking. Comput. Phys. Commun. 309, 109497 (2025).
Aehle, M. et al. Optimization using pathwise algorithmic derivatives of electromagnetic shower simulations. Comput. Phys. Commun. 309, 109491 (2025).
Aehle, M., Blühdorn, J., Sagebaum, M. & Gauger, N. R. Forward-mode automatic differentiation of compiled programs. ACM Trans. Math. Softw. 51, 1–25 (2025).
Aehle, M. et al. Efficient forward-mode algorithmic derivatives of Geant4. Preprint at https://doi.org/10.48550/arXiv.2407.02966 (2024).
Christodoulou, S. & Naumann, U. Differentiable programming: efficient smoothing of control-flow-induced discontinuities. Preprint at https://doi.org/10.48550/arXiv.2305.06692 (2023).
Arya, G., Schauer, M., Schäfer, F. & Rackauckas, C. Automatic differentiation of programs with discrete randomness. Adv. Neural Inf. Process. Syst. 35, 10435–10447 (2022).
Aehle, M. et al. Exploration of differentiability in a proton computed tomography simulation framework. Phys. Med. Biol. 68, 244002 (2023).
Silvano, C. et al. A survey on deep learning hardware accelerators for heterogeneous HPC platforms. ACM Comput. Surv. 57, 1–39 (2025).
Li, M. et al. The deep learning compiler: a comprehensive survey. IEEE Trans. Parallel Distrib. Syst. 32, 708–727 (2020).
Aycock, J. A brief history of just-in-time. ACM Comput. Surv. (CSUR) 35, 97–113 (2003).
Bociort, F. Why are there so many system shapes in lens design? Proc. SPIE 7849, 114–126 (2010).
Li, H., Xu, Z., Taylor, G., Studer, C. & Goldstein, T. Visualizing the loss landscape of neural nets. Adv. Neural Inf. Process. Syst. 31, 6391 (2018).
Richardson, J. W., Pandey, S., Bytyqi, E., Edo, T. & Adhikari, R. X. Optimizing gravitational-wave detector design for squeezed light. Phys. Rev. D 105, 102002 (2022).
Meyer, J. J., Borregaard, J. & Eisert, J. A variational toolbox for quantum multi-parameter estimation. npj Quantum Inf. 7, 89 (2021).
O’Driscoll, L., Nichols, R. & Knott, P. A. A hybrid machine learning algorithm for designing quantum experiments. Quantum Mach. Intell. 1, 5–15 (2019).
Dorigo, T. et al. On the codesign of scientific experiments and industrial systems. Preprint at https://doi.org/10.48550/arXiv.2603.26613 (2026).
Nichols, R., Mineh, L., Rubio, J., Matthews, J. C. & Knott, P. A. Designing quantum experiments with a genetic algorithm. Quantum Sci. Technol. 4, 045012 (2019).
Bindel, D., Landreman, M. & Padidar, M. Understanding trade-offs in stellarator design with multi-objective optimization. J. Plasma Phys. 89, 905890503 (2023).
Ruiz, F. J. et al. Quantum circuit optimization with AlphaTensor. Nat. Mach. Intell. 7, 374–385 (2025).
Zen, R., Nägele, M. & Marquardt, F. Reusability report: optimizing T count in general quantum circuits with AlphaTensor-Quantum. Nat. Mach. Intell. 8, 113–117 (2025).
Venugopalan, G., Salces-Cárcoba, F., Arai, K. & Adhikari, R. X. Global optimization of multilayer dielectric coatings for precision measurements. Opt. Express 32, 11751–11762 (2024).
McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R. & Neven, H. Barren plateaus in quantum neural network training landscapes. Nat. Commun. 9, 4812 (2018).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In Proc. International Conference on Learning Representations (ICLR) (2015).
Krenn, M., Kottmann, J. S., Tischler, N. & Aspuru-Guzik, A. Conceptual understanding through efficient automated design of quantum optical experiments. Phys. Rev. X 11, 031044 (2021).
Landreman, M. et al. SIMSOPT: a flexible framework for stellarator optimization. J. Open Source Softw. 6, 3525 (2021).
Strong, G. C. et al. TomOpt: differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography. Mach. Learn. Sci. Technol. 5, 035002 (2024).
Zaher, Z. et al. Optimization of a cosmic muon tomography scanner for cargo border control inspection. J. Appl. Phys. 138, 194903 (2025).
Hvarfner, C., Hellsten, E. O. & Nardi, L. Vanilla Bayesian optimization performs great in high dimensions. In Proc. 41st International Conference on Machine Learning (ICML’24) 20793–20817 (PMLR, 2024).
Giuliani, A. Direct stellarator coil design using global optimization: application to a comprehensive exploration of quasi-axisymmetric devices. J. Plasma Phys. 90, 905900303 (2024).
Roussel, R. et al. Bayesian optimization algorithms for accelerator physics. Phys. Rev. Accel. Beams 27, 084801 (2024).
Flam-Shepherd, D. et al. Learning interpretable representations of entanglement in quantum optics experiments using deep generative models. Nat. Mach. Intell. 4, 544–554 (2022).
Olle, J., Zen, R., Puviani, M. & Marquardt, F. Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent. npj Quantum Inf. 10, 126 (2024).
Trenkwalder, L. M., López-Incera, A., Nautrup, H. P., Flamini, F. & Briegel, H. J. Automated gadget discovery in the quantum domain. Mach. Learn. Sci. Technol. 4, 035043 (2023).
Sarra, L., Ellis, K. & Marquardt, F. Discovering quantum circuit components with program synthesis. Mach. Learn. Sci. Technol. 5, 025029 (2024).
Kundu, A. & Sarra, L. Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware. Commun. Phys. 9, 44 (2026).
Melnikov, A. A., Sekatski, P. & Sangouard, N. Setting up experimental Bell tests with reinforcement learning. Phys. Rev. Lett. 125, 160401 (2020).
Valcarce, X., Sekatski, P., Gouzien, E., Melnikov, A. & Sangouard, N. Automated design of quantum-optical experiments for device-independent quantum key distribution. Phys. Rev. A 107, 062607 (2023).
Landgraf, J., Peano, V. & Marquardt, F. Automated discovery of coupled-mode setups. Phys. Rev. X 15, 021038 (2025). This paper introduces autoscatter, an AI-driven discovery framework for interpretable wave-scattering devices that can find counterintuitive and simpler architectures than human designs.
Côté, G., Lalonde, J.-F. & Thibault, S. Deep learning-enabled framework for automatic lens design starting point generation. Opt. Express 29, 3841–3854 (2021).
Fürrutter, F., Chandani, Z., Hamamura, I., Briegel, H. J. & Muñoz-Gil, G. Synthesis of discrete–continuous quantum circuits with multimodal diffusion models. Mach. Learn. Sci. Technol. 7, 025065 (2026).
Zoccheddu, S., Qasim, S. R., Owen, P. & Serra, N. Large language models for physics instrument design. Mach. Learn. Sci. Technol. 7, 045005 (2026).
Wang, H. et al. Efficient evolutionary search over chemical space with large language models. In Proc. Thirteenth International Conference on Learning Representations (ICLR 2025) (2025).
Lu, D., Malof, J. M. & Padilla, W. J. An agentic framework for autonomous metamaterial modeling and inverse design. ACS Photonics 12, 6071–6080 (2025).
Barman, K. G. et al. Large physics models: towards a collaborative approach with large language models and foundation models. Eur. Phys. J. C 85, 1066 (2025).
Foreback, M. et al. ECLIPSE: an Evolutionary Computation Library for Instrumentation Prototyping in Scientific Engineering. Preprint at https://doi.org/10.48550/arXiv.2601.05098 (2026).
Fasoli, A. et al. Computational challenges in magnetic-confinement fusion physics. Nat. Phys. 12, 411–423 (2016).
Lauro, L. D. et al. Optimization methods for integrated and programmable photonics in next-generation classical and quantum smart communication and signal processing systems. Adv. Opt. Photonics 17, 526–622 (2025).
Kostelecký, V. A. & Russell, N. Data tables for Lorentz and CPT violation. Rev. Mod. Phys. 83, 11–31 (2011). This paper presents a systematic extension of the standard model with hundreds of parameters, each of them being a potential objective for AI-driven experimental design.
Russell, N. E. Mining the Data Tables for Lorentz and CPT Violation. In Proc. Eighth Meeting on CPT and Lorentz Symmetry 82–85 (World Scientific, 2020).
Will, C. M. in General Relativity and John Archibald Wheeler 73–93 (Springer, 2010).
DeMille, D., Doyle, J. M. & Sushkov, A. O. Probing the frontiers of particle physics with tabletop-scale experiments. Science 357, 990–994 (2017).
Hamilton, P. et al. Atom-interferometry constraints on dark energy. Science 349, 849–851 (2015).
Pikovski, I., Vanner, M. R., Aspelmeyer, M., Kim, M. & Brukner, Č. Probing Planck-scale physics with quantum optics. Nat. Phys. 8, 393–397 (2012).
Lo, A. et al. Acoustic tests of Lorentz symmetry using quartz oscillators. Phys. Rev. X 6, 011018 (2016).
Arndt, M. & Hornberger, K. Testing the limits of quantum mechanical superpositions. Nat. Phys. 10, 271–277 (2014).
Safronova, M. et al. Search for new physics with atoms and molecules. Rev. Mod. Phys. 90, 025008 (2018).
Arlt, S., Gu, X. & Krenn, M. Towards autonomous quantum physics research using LLM agents with access to intelligent tools. Preprint at https://doi.org/10.48550/arXiv.2511.11752 (2025).
Baek, J., Jauhar, S. K., Cucerzan, S. & Hwang, S. J. ResearchAgent: iterative research idea generation over scientific literature with large language models. In Proc. 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) 6709–6738 (Association for Computational Linguistics, 2025).
Gu, X. & Krenn, M. Forecasting high-impact research topics via machine learning on evolving knowledge graphs. Mach. Learn. Sci. Technol. 6, 025041 (2025).
Sourati, J. & Evans, J. A. Accelerating science with human-aware artificial intelligence. Nat. Hum. Behav. 7, 1682–1696 (2023).
Cadena, S. A. et al. ConStellaration: a dataset of QI-like stellarator plasma boundaries and optimization benchmarks. Adv. Neural Inf. Process. Syst. 38, 17351–17377 (2025).
Elitez, D. et al. ColliderML: the first release of an OpenDataDetector high-luminosity physics benchmark dataset. In Machine Learning and the Physical Sciences Workshop at NeurIPS 2025 (2025).
Carleo, G. & Troyer, M. Solving the quantum many-body problem with artificial neural networks. Science 355, 602–606 (2017).
De Regt, H. W. Understanding Scientific Understanding (Oxford Univ. Press, 2017).
Krenn, M. et al. On scientific understanding with artificial intelligence. Nat. Rev. Phys. 4, 761–769 (2022).
Brown, D. D. et al. FINESSE. Zenodo https://doi.org/10.5281/zenodo.12662017 (2025).
The LIGO Scientific Collaboration et al. Advanced LIGO. Class. Quantum Gravity 32, 074001 (2015).