AI tool lets researchers 'vibe code' in the quantum realm - Nature

Email Bluesky Facebook LinkedIn Reddit Whatsapp X Save article View saved research Quantum computers, such as this one shown at this year’s Mobile World Congress in Barcelona, Spain, could run programs that have been almost completely generated by artificial-intelligence tools. Credit: Angel Garcia/Bloomberg/Getty ‘Vibe coding’ is moving into quantum computing — a field in which programming has notoriously required sophisticated skills. To lower this barrier, researchers at Pasqal, a quantum-computing start-up company in Paris, have developed an artificial-intelligence tool that can turn an English-language prompt into quantum computing code and then autonomously run it on a quantum computer. The agent, which is described in a preprint posted on the arXiv server last month 1 , often requires feedback from people with specialized knowledge to work properly. But its creators say that it still accelerates the work and it could make quantum computers — machines that can greatly speed up certain calculations by harnessing quantum phenomena — accessible to a wide range of researchers. Christophe Jurczak, a co-author of the work and one of Pasqal’s co-founders, says the agent enabled him to run experiments that would commonly require a team of physicists who are highly specialized in quantum-computing. “And I can do it on my own, from my couch in Dallas, Texas.” Quantum vibes In classical, as opposed to quantum, computer programming, vibe coding refers to an extreme version of AI-aided software engineering, in which the user describes to an AI tool what they want a piece of software to do, and the machine — perhaps after a few adjustments according to the user’s feedback — produces and runs a fully working program. Quantum simulations verified by experiments for the first time Frontier large language models (LLMs), such as Anthropic’s Claude, have displayed a grasp of quantum computing, and many researchers now use them to help write not just classical but also quantum code. To see whether such LLMs could go as far as quantum vibe coding, Jurczak and his collaborators familiarized frontier LLMs with the technical specifications of Pasqal’s quantum computers. They aimed specifically at streamlining the development of one of the most promising applications of quantum computers — quantum simulations . These involve tuning a quantum computer to resemble the behaviour of another physical system, such as a catalyst or a material with unusual magnetic properties. The quantum machine running the simulation could then predict the materials’ properties with calculations that would overwhelm a classical computer. In their study, the Pasqal team subjected the AI agent to three tests. In each case, they chose a physics paper describing a physical phenomenon that could, in principle, be simulated on a quantum computer, and asked the agent to write and execute the code to confirm this. Two of the test cases involved simulating materials in which atoms flip up or down, like tiny bar magnets, depending on the orientation of their neighbours. To model this phenomenon, the agent has to first translate it into a possible behaviour of a Pasqal machine — a quantum computer that encodes information in an array of atoms trapped with laser light. This crucial ‘translation’ step has typically required a team with expertise both in the physics of the materials being simulated and in quantum computing, says physicist Loïc Henriet, chief technology officer at Pasqal and a co-author of the study. But before running the quantum code on actual Pasqal quantum computers, the agent first tests it using ‘virtual’ versions that run on a classical computer. If the quantum code passes that test, it is then automatically sent to one of Pasqal’s quantum computers in Dhahran, Saudi Arabia, or to another in Sherbrooke, Canada. In all three tests, the agent demonstrated “a firm grasp of the hardware constraints”, the authors write. In one case, the authors tried to trick the agent by asking it to do a simulation that they knew would be too complicated to realize on the Pasqal machines available on the cloud, and the agent correctly explained why it couldn’t be done. In another, the agent required a lot of hand-holding from the human researcher to reach a “physically accurate implementation”, the authors say. Enjoying our latest content? Log in or create an account to continue Access the most recent journalism from Nature's award-winning team Explore the latest features & opinion covering groundbreaking research Access through your institution or Sign in or create an account Continue with Google Continue with ORCiD doi: https://doi.org/10.1038/d41586-026-02495-4 References Dalyac, C., Dauphin, A., Henriet, L. & Jurczak, C. Preprint at arXiv https://doi.org/10.48550/arXiv.2607.25834 (2026). Basit, A. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2503.02497 (2025). Shiraishi, M., Hamamura, I., Ishigaki, T. & Kadowaki, T. Preprint at arXiv https://doi.org/10.48550/arXiv.2604.08318 (2026). 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