Digital Discovery of 100 diverse Quantum Experiments with PyTheus

Digital Discovery of 100 diverse Quantum Experiments with PyTheus

Source Node: 3012456

Abstract

Photons are the physical system of choice for performing experimental tests of the foundations of quantum mechanics. Furthermore, photonic quantum technology is a main player in the second quantum revolution, promising the development of better sensors, secure communications, and quantum-enhanced computation. These endeavors require generating specific quantum states or efficiently performing quantum tasks. The design of the corresponding optical experiments was historically powered by human creativity but is recently being automated with advanced computer algorithms and artificial intelligence. While several computer-designed experiments have been experimentally realized, this approach has not yet been widely adopted by the broader photonic quantum optics community. The main roadblocks consist of most systems being closed-source, inefficient, or targeted to very specific use-cases that are difficult to generalize. Here, we overcome these problems with a highly-efficient, open-source digital discovery framework PyTheus, which can employ a wide range of experimental devices from modern quantum labs to solve various tasks. This includes the discovery of highly entangled quantum states, quantum measurement schemes, quantum communication protocols, multi-particle quantum gates, as well as the optimization of continuous and discrete properties of quantum experiments or quantum states. PyTheus produces interpretable designs for complex experimental problems which human researchers can often readily conceptualize. PyTheus is an example of a powerful framework that can lead to scientific discoveries – one of the core goals of artificial intelligence in science. We hope it will help accelerate the development of quantum optics and provide new ideas in quantum hardware and technology.

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[12] Tareq Jaouni, Xiaoqin Gao, Sören Arlt, Mario Krenn, and Ebrahim Karimi, "Experimental Solutions to the High-Dimensional Mean King's Problem", arXiv:2307.12938, (2023).

[13] Zeqiao Zhou, Yuxuan Du, Xu-Fei Yin, Shanshan Zhao, Xinmei Tian, and Dacheng Tao, "Optical Quantum Sensing for Agnostic Environments via Deep Learning", arXiv:2311.07203, (2023).

[14] Carla Rodríguez, Sören Arlt, Leonhard Möckl, and Mario Krenn, "XLuminA: An Auto-differentiating Discovery Framework for Super-Resolution Microscopy", arXiv:2310.08408, (2023).

The above citations are from SAO/NASA ADS (last updated successfully 2023-12-13 13:35:00). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref's cited-by service no data on citing works was found (last attempt 2023-12-13 13:34:58).

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