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  • Letter
  • Open Access

Automated discovery of nonlocal photonic gates

Sören Arlt*

Mario Krenn†

Xuemei Gu‡

  • Machine Learning in Science Cluster, Department of Computer Science, Faculty of Science, University of Tübingen, Tübingen, Germany

  • *Contact author: soeren.arlt@uni-tuebingen.de
  • †Contact author: mario.krenn@uni-tuebingen.de
  • ‡Contact author: xuemei.gu@uni-jena.de
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Phys. Rev. Research 8, L022031 – Published 18 May, 2026

DOI: https://doi.org/10.1103/f415-kgwr

Abstract

Interactions between quantum systems enable quantum gates, the building blocks of quantum information processing. In photonics, direct photon-photon interactions are too weak to be practically useful, so effective interactions are engineered using linear optics and measurement. A central challenge is to realize such interactions nonlocally, i.e., between photons that remain spatially separated. We present experimental proposals for several essential nonlocal multiphoton quantum gates acting on spatially separated photons, in both qubit and high-dimensional qudit systems. Rather than relying on preshared entanglement or Bell-state measurements, our gates use quantum indistinguishability via path identity as a resource, exploiting coherent superpositions of photon-pair origins. The mechanisms of the solutions share many properties with quantum teleportation, but do not require shared entanglement or Bell-state measurements. The underlying idea was generated by the multiagent large language model (LLM) system AI-Mandel, which autonomously implemented its proposed idea using the AI-driven discovery system PyTheus. The human authors then interpreted, generalized, and contextualized the resulting solutions. Technically, our results establish path indistinguishability as a practical resource for distributed quantum information processing; conceptually, they demonstrate how automated discovery systems can contribute ideas and techniques in physics.

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Phys. Rev. Research 8, L022031– Published 18 May, 2026
  • Received 12 January 2026
  • Accepted 13 April 2026
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