Photonic technologies are main players in the second quantum revolution, providing better sensors, secure communications, and quantum-enhanced computation. Such endeavors require generating specific quantum states or efficiently performing quantum tasks. The design of the corresponding optical experiments, historically powered by human creativity, is being slowly automated by novel algorithms. Unfortunately, these tools are often restricted to very specific use cases and are difficult to generalize, which limits their practical implementation. To overcome these challenges, we developed PyTheus, a highly efficient, open-source digital discovery framework based on a graph-based representation of optical setups, which includes a wide range of modern experimental devices. PyTheus produces interpretable designs to solve complex experimental problems, like entanglement generation and distribution. Aiming for the simplest solutions, our software provides deeper understanding to human researchers, which can generalize the findings. Therefore, PyTheus can accelerate the development of quantum optics and related technologies.
Photonic technologies are main players in the second quantum revolution, providing better sensors, secure communications, and quantum-enhanced computation. Such endeavors require generating specific quantum states or efficiently performing quantum tasks. The design of the corresponding optical experiments, historically powered by human creativity, is being slowly automated by novel algorithms. Unfortunately, these tools are often restricted to very specific use cases and are difficult to generalize, which limits their practical implementation. To overcome these challenges, we developed PyTheus, a highly efficient, open-source digital discovery framework based on a graph-based representation of optical setups, which includes a wide range of modern experimental devices. PyTheus produces interpretable designs to solve complex experimental problems, like entanglement generation and distribution. Aiming for the simplest solutions, our software provides deeper understanding to human researchers, which can generalize the findings. Therefore, PyTheus can accelerate the development of quantum optics and related technologies.
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.
Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general.One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding.Scientists have used AI techniques to rediscover previously known concepts.So far, no examples of that kind have been reported that are applied to open problems for getting new scientific concepts and ideas.Here, we present THESEUS, an algorithm that can provide new conceptual understanding, and we demonstrate its applications in the field of experimental quantum optics.To do so, we make four crucial contributions.(i) We introduce a graph-based representation of quantum optical experiments that can be interpreted and used algorithmically.(ii) We develop an automated design approach for new quantum experiments, which is orders of magnitude faster than the best previous algorithms at concrete design tasks for experimental configuration.(iii) We solve several crucial open questions in experimental quantum optics which involve practical blueprints of resource states in photonic quantum technology and quantum states and transformations that allow for new foundational quantum experiments.Finally, and most importantly, (iv) the interpretable representation and enormous speed-up allow us to produce solutions that a human scientist can interpret and gain new scientific concepts from outright.We anticipate that THESEUS will become an essential tool in quantum optics for developing new experiments and photonic hardware.It can further be generalized to answer open questions and provide new concepts in a large number of other quantum physical questions beyond quantum optical experiments.THESEUS is a demonstration of explainable AI (XAI) in physics that shows how AI algorithms can contribute to science on a conceptual level.
We present a modular interpreter for PyTheus-optimized quantum networks that automatically analyzes and visualizes complex quantum architectures discovered through automated optimization. The interpreter addresses the critical challenge of understanding machine-designed quantum networks by providing robust algorithms for functional role identification, graph-theoretical analysis, and physically meaningful visualization across the major classes of PyTheus-generated networks. Our interpreter accepts both file-based and in-memory network representations, automatically identifies sources, detectors, beam splitters, and ancillas through priority-based classification, and generates coordinated native graph plots and optical table representations. We demonstrate the interpreter's capabilities through two complementary approaches: (1) analysis of a newly developed five-node quantum key distribution network that reveals distributed source architecture and dual-role node functionality, and (2) comprehensive validation using existing PyTheus examples including W4 state generation, heralded Bell state preparation, and GHZ state networks. The interpreter successfully handles complex connectivity patterns across diverse quantum network architectures within the tested classes, avoids visualization artifacts, and provides validation mechanisms for architectural consistency. Our primary contribution is the development of robust modular interpretation algorithms that can analyze the major classes of PyTheus-generated quantum networks, enabling better understanding of automated quantum architecture design.
Physics simulators can support the discovery of quantum experiments by predicting the states generated by experimental configurations. When these simulators are computationally expensive, repeated simulator calls can limit the search for experiments that generate a desired quantum state. Here, we develop a physics-inspired polynomial neural surrogate for PyTheus, a graph-based quantum-optics simulator, to predict quantum states and use it to design quantum experiments. Its polynomial activations are motivated by the relation between graph perfect matchings and the resulting state amplitudes. We train separate surrogate models for four-, six-, and eight-photon systems and show that they achieve higher prediction accuracy with fewer trainable parameters than standard multilayer perceptrons. We then use the trained surrogates for inverse design of GHZ, W, and linear-cluster states. For the larger systems, the surrogates also enable faster inverse design than direct optimization with PyTheus. These results suggest that incorporating the underlying physics into neural surrogates can provide an efficient approach to quantum experiment design.
Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding. Scientists have used AI techniques to rediscover previously known concepts. So far, no examples of that kind have been reported that are applied to open problems for getting new scientific concepts and ideas. Here, we present Theseus, an algorithm that can provide new conceptual understanding, and we demonstrate its applications in the field of experimental quantum optics. To do so, we make four crucial contributions. (i) We introduce a graph-based representation of quantum optical experiments that can be interpreted and used algorithmically. (ii) We develop an automated design approach for new quantum experiments, which is orders of magnitude faster than the best previous algorithms at concrete design tasks for experimental configuration. (iii) We solve several crucial open questions in experimental quantum optics which involve practical blueprints of resource states in photonic quantum technology and quantum states and transformations that allow for new foundational quantum experiments. Finally, and most importantly, (iv) the interpretable representation and enormous speed-up allow us to produce solutions that a human scientist can interpret and gain new scientific concepts from outright. We anticipate that Theseus will become an essential tool in quantum optics for developing new experiments and photonic hardware. It can further be generalized to answer open questions and provide new concepts in a large number of other quantum physical questions beyond quantum optical experiments. Theseus is a demonstration of explainable AI (XAI) in physics that shows how AI algorithms can contribute to science on a conceptual level.
Vaidman, Aharanov, and Albert [ Phys. Rev. Lett. 58 ( 14 ), 1385 ( 1987 ) 10.1103/PhysRevLett.58.1385 ] put forward a puzzle called the mean king’s problem (MKP) that can be solved only by harnessing quantum entanglement. Prime-powered solutions to the problem have been shown to exist, but they have not yet been experimentally realized for any dimension beyond two. We propose a general first-of-its-kind experimental scheme for solving the MKP in prime dimensions ( D ). Our search is guided by the digital discovery framework Pytheus , which finds highly interpretable graph-based representations of quantum optical experimental setups; using it, we find specific solutions and generalize to higher dimensions through human insight. As proof of principle, we present a detailed investigation of our solution for the three-, five-, and seven-dimensional cases. We obtain maximum success probabilities of 82.3%, 56.2%, and 35.5%, respectively. We therefore posit that our computer-inspired scheme yields solutions that implement Alice’s strategy with quantum advantage, demonstrating its promise for experimental implementation in quantum communication tasks.