arXiv subjects

Mario Krenn

Publications and source records attributed to Mario Krenn.

At least 19 recordsLinked to original sources

arXiv2026

Photonic realization of a subgraph extraction in a quantum random network

Understanding how complex connectivity emerges in networks is a fundamental challenge in classical and quantum science. In classical random networks, complex subgraphs typically require relatively high connection probabilities, whereas quantum random network theory predicts that such structures can arise at a single, lower threshold through entanglement and local operations. Here, using an integrated silicon photonic chip, we experimentally realize a quantum subgraph predicted by quantum random network theory in a four-node quantum random network. Our integrated platform exploits probabilistic photon-pair sources and coherent control of path modes to prepare a structured quantum subgraph through local transformations and postselection, operating in a threshold regime that differs from classical random networks. We verify that the subgraph state exhibits genuine high-dimensional multipartite entanglement across the nodes, providing experimental evidence that quantum entanglement enables connectivity structures beyond classical accessibility.

quant-ph↗

arXiv2026

Automated discovery of high-probability heralded schemes for path-entangled states

Entangled states of light lie at the heart of photonic quantum technologies, from distributed quantum communication to quantum-enhanced measurement and information processing. Their practical generation, however, remains constrained by the weak interactions between photons, which make the deterministic assembly of large multiphoton entangled states a central challenge in quantum optics. In this work, we use AI techniques to discover heralded linear-optical schemes for path-entangled states and show that the resulting solutions can be elevated from individual circuits to a new scalable family. This family contains previously known constructions as special cases while generally providing exponential and super-exponential improvements over those, and its extension to broader classes of target states shows how automated discovery can reveal transferable physical understanding. By presenting compact experimental proposals for large path-entangled states, our results provide both a theoretical advance in photonic heralding and a route towards a substantial leap in experimentally accessible multiphoton entanglement.

quant-ph↗

arXiv2026

Automated experimental design for high-probability entanglement generation

Entangled photons are widely used in quantum technologies. Many photonic experiments generate them with probabilistic photon-pair sources that can be modeled as squeeze operators. In practice, these sources are usually treated in the low-gain (perturbative) regime, keeping only the leading single-pair term and neglecting higher-order multi-pair emission events. In pursuit of fidelity, the probability of successful entanglement generation can become extremely small, a tradeoff often ignored. Here we develop an automated design algorithm for quantum experiments to optimize both fidelity and success probability while accounting for higher-order multi-pair emissions. Our discovery algorithm explores different design topologies subject to varying hardware constraints. It optimizes the source parameters to reduce undesired higher-order terms or even benefit from them. The experiments presented outperform previous proposals for widely used states, including heralded Bell states, W states, and NOON states, paving the way for more efficient photonic technologies.

quant-ph↗

arXiv2025

Neural surrogates for designing gravitational wave detectors

Physics simulators are essential in science and engineering, enabling the analysis, control, and design of complex systems. In experimental sciences, they are increasingly used to automate experimental design, often via combinatorial search and optimization. However, as the setups grow more complex, the computational cost of traditional, CPU-based simulators becomes a major limitation. Here, we show how neural surrogate models can significantly reduce reliance on such slow simulators while preserving accuracy. Taking the design of interferometric gravitational wave detectors as a representative example, we train a neural network to surrogate the gravitational wave physics simulator Finesse, which was developed by the LIGO community. Despite that small changes in physical parameters can change the output by orders of magnitudes, the model rapidly predicts the quality and feasibility of candidate designs, allowing an efficient exploration of large design spaces. Our algorithm loops between training the surrogate, inverse designing new experiments, and verifying their properties with the slow simulator for further training. Assisted by auto-differentiation and GPU parallelism, our method proposes high-quality experiments much faster than direct optimization. Solutions that our algorithm finds within hours outperform designs that take five days for the optimizer to reach. Though shown in the context of gravitational wave detectors, our framework is broadly applicable to other domains where simulator bottlenecks hinder optimization and discovery.

cs.LG↗

arXiv2025

Analytical Fock Representation of Two-Mode Squeezing for Quantum Interference

Two-mode squeezing is central to entangled-photon generation and nonlinear interferometry, yet standard perturbative low-gain treatments and Gaussian formalisms can obscure the interference of photon-number amplitudes, especially in nonlinear interferometers and at high gain. Here we derive a closed-form Fock-basis expression for the action of the two-mode squeezing operator on arbitrary number states, enabling the direct analysis of nonlinear interferometers in the photon-number basis at arbitrary squeezing strength. Within this framework, we provide intuitive physical interpretations of several known quantum-interference effects and identify a new multi-photon interference phenomenon in a four-crystal geometry that could readily be observed in laboratories. Our work provides a compact analytic toolkit and explicit design rules for engineering multi-photon interference, with applications in quantum sensing, precision metrology, and quantum state generation.

quant-ph↗

arXiv2025

Towards autonomous quantum physics research using LLM agents with access to intelligent tools

Artificial intelligence (AI) is used in numerous fields of science, yet the initial research questions and targets are still almost always provided by human researchers. AI-generated creative ideas in science are rare and often vague, so that it remains a human task to execute them. Automating idea generation and implementation in one coherent system would significantly shift the role of humans in the scientific process. Here we present AI-Mandel, an LLM agent that can generate and implement ideas in quantum physics. AI-Mandel formulates ideas from the literature and uses a domain-specific AI tool to turn them into concrete experiment designs that can readily be implemented in laboratories. The generated ideas by AI-Mandel are often scientifically interesting - for two of them we have already written independent scientific follow-up papers. The ideas include new variations of quantum teleportation, primitives of quantum networks in indefinite causal orders, and new concepts of geometric phases based on closed loops of quantum information transfer. AI-Mandel is a prototypical demonstration of an AI physicist that can generate and implement concrete, actionable ideas. Building such a system is not only useful to accelerate science, but it also reveals concrete open challenges on the path to human-level artificial scientists.

cs.AI↗

arXiv2025

Automated Discovery of Non-local Photonic Gates

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 with linear optics and measurement. A central challenge is to realize such interactions non-locally, i.e., between photons that remain spatially separated. We present experimental proposals for several essential non-local multiphoton quantum gates that act on spatially separated photons, in both qubit and high-dimensional qudit systems. All solutions were discovered by the AI-driven discovery system called PyTheus. Rather than using pre-shared entanglement or Bell state measurements, our gates use as a resource quantum indistinguishability by path identity - a technique that exploits coherent superpositions of the photon pair origins. While analyzing these solutions, we uncovered a new mechanism that mimics much of the properties of quantum teleportation, without shared entanglement or Bell state measurements. Technically, our results establish path indistinguishability as a practical resource for distributed quantum information processing; conceptually, they demonstrate how automated discovery systems can contribute new ideas and techniques in physics.

quant-ph↗

arXiv2025

Automated discovery of high-dimensional multipartite entanglement with photons that never interacted

Quantum entanglement across spatially separated network nodes is conventionally established through the distribution of photons from a common source or via entanglement swapping that relies on Bell-state measurements and pre-shared entanglement. Path identity, where the emission origins of photons from different sources are made indistinguishable, offers an alternative route. We show that this mechanism enables complex multipartite, high-dimensional, and even logical entanglement between remote nodes whose photons never interacted. Our schemes require neither direct photon interaction, pre-shared entanglement, nor Bell-state measurements, highlighting a distinct resource for distributed quantum communication and computation. All of the solutions were discovered automatically using highly efficient computational design tools, indicating the potential for scientific inspiration from computational algorithms.

quant-ph↗

arXiv2025

Violation of Bell Inequality with Unentangled Photons

Violation of local realism via Bell inequality - a profound and counterintuitive manifestation of quantum theory that conflicts with the prediction of local realism - is viewed to be intimately linked with quantum entanglement. Experimental demonstrations of such a phenomenon using quantum entangled states are among the landmark experiments of modern physics and paved the way for quantum technology. Here we report the violation of the Bell inequality that cannot be described by quantum entanglement in the system but arises from quantum indistinguishability by path identity, shown by the multi-photon frustrated interference. By analyzing the measurement of four-photon frustrated interference within the standard Bell-test formalism, we find a violation of Bell inequality by more than four standard deviations. Our work establishes a connection between quantum correlation and quantum indistinguishability, providing insights into the fundamental origin of the counterintuitive characteristics observed in quantum physics.

quant-ph↗

arXiv2025

Quantum computing and artificial intelligence: status and perspectives

This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could support the development of innovative AI solutions. It also examines use cases of classical AI that can empower research and development in quantum technologies, with a focus on quantum computing and quantum sensing. The purpose of this white paper is to provide a long-term research agenda aimed at addressing foundational questions about how AI and quantum computing interact and benefit one another. It concludes with a set of recommendations and challenges, including how to orchestrate the proposed theoretical work, align quantum AI developments with quantum hardware roadmaps, estimate both classical and quantum resources - especially with the goal of mitigating and optimizing energy consumption - advance this emerging hybrid software engineering discipline, and enhance European industrial competitiveness while considering societal implications.

quant-ph↗

arXiv2025

Tutorial: Hong-Ou-Mandel interference with Structured Photons

The Hong-Ou-Mandel (HOM) effect, an effective two-photon interference phenomenon, is a cornerstone of quantum optics and a key tool for linear optical quantum information processing. While the HOM effect has been extensively studied both theoretically and experimentally for various photonic quantum states, particularly in the spectral domain, detailed overviews of its behaviour for structured photons -- those with complex spatial profiles -- under arbitrary spatial mode measurement schemes are still lacking. This tutorial aims to fill this gap by providing a comprehensive theoretical analysis of the HOM effect for structured photons, including an arbitrary mode projection on quantum interference outcomes. The tutorial also provides analytical, closed-form expressions of the HOM visibility under different measurement conditions, which is a crucial contribution for its application in computational and artificial-intelligence-driven discovery of new quantum experiments exploiting the power of photons with complex spatial modes.

quant-ph↗

arXiv2024

Entangling independent particles by path identity

Quantum entanglement -- correlations of particles that are stronger than any classical analogue -- is the basis for research on the foundations of quantum mechanics and for practical applications such as quantum networks. Traditionally, entanglement is achieved through local interactions or via entanglement swapping, where entanglement at a distance is generated through previously established entanglement and Bell-state measurements. However, the precise requirements enabling the generation of quantum entanglement without traditional local interactions remain less explored. Here we demonstrate that independent particles can be entangled without the need for direct interaction, prior established entanglement, or Bell-state measurements, by exploiting the indistinguishability of the origins of photon pairs. Our demonstrations challenge the long-standing belief that the prior generation and measurement of entanglement are necessary prerequisites for generating entanglement between independent particles that do not share a common past. In addition to its foundational interest, we show that this technique might lower the resource requirements in quantum networks, by reducing the complexity of photon sources and the overhead photon numbers.

quant-ph↗

arXiv2024

Discovering emergent connections in quantum physics research via dynamic word embeddings

As the field of quantum physics evolves, researchers naturally form subgroups focusing on specialized problems. While this encourages in-depth exploration, it can limit the exchange of ideas across structurally similar problems in different subfields. To encourage cross-talk among these different specialized areas, data-driven approaches using machine learning have recently shown promise to uncover meaningful connections between research concepts, promoting cross-disciplinary innovation. Current state-of-the-art approaches represent concepts using knowledge graphs and frame the task as a link prediction problem, where connections between concepts are explicitly modeled. In this work, we introduce a novel approach based on dynamic word embeddings for concept combination prediction. Unlike knowledge graphs, our method captures implicit relationships between concepts, can be learned in a fully unsupervised manner, and encodes a broader spectrum of information. We demonstrate that this representation enables accurate predictions about the co-occurrence of concepts within research abstracts over time. To validate the effectiveness of our approach, we provide a comprehensive benchmark against existing methods and offer insights into the interpretability of these embeddings, particularly in the context of quantum physics research. Our findings suggest that this representation offers a more flexible and informative way of modeling conceptual relationships in scientific literature.

cs.LG↗

arXiv2024

Predicting atmospheric turbulence for secure quantum communications in free space

Atmospheric turbulence is the main barrier to large-scale free-space quantum communication networks. Aberrations distort optical information carriers, thus limiting or preventing the possibility of establishing a secure link between two parties. For this reason, forecasting the turbulence strength within an optical channel is highly desirable, as it allows for knowing the optimal timing to establish a secure link in advance. Here, we train a Recurrent Neural Network, TAROCCO, to predict the turbulence strength within a free-space channel. The training is based on weather and turbulence data collected over 9 months for a 5.4 km intra-city free-space link across the City of Ottawa. The implications of accurate predictions from our network are demonstrated in a simulated high-dimensional Quantum Key Distribution protocol based on orbital angular momentum states of light across different turbulence regimes. TAROCCO will be crucial in validating a free-space channel to optimally route the key exchange for secure communications in real experimental scenarios.

quant-ph↗

arXiv2024

Meta-Designing Quantum Experiments with Language Models

Artificial Intelligence (AI) can solve complex scientific problems beyond human capabilities, but the resulting solutions offer little insight into the underlying physical principles. One prominent example is quantum physics, where computers can discover experiments for the generation of specific quantum states, but it is unclear how finding general design concepts can be automated. Here, we address this challenge by training a transformer-based language model to create human-readable Python code, which solves an entire class of problems in a single pass. This strategy, which we call meta-design, enables scientists to gain a deeper understanding and extrapolate to larger experiments without additional optimization. To demonstrate the effectiveness of our approach, we uncover previously unknown experimental generalizations of important quantum states, e.g. from condensed matter physics. The underlying methodology of meta-design can naturally be extended to fields such as materials science or engineering.

quant-ph↗

arXiv2024

Interesting Scientific Idea Generation using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders

The rapid growth of scientific literature makes it challenging for researchers to identify novel and impactful ideas, especially across disciplines. Modern artificial intelligence (AI) systems offer new approaches, potentially inspiring ideas not conceived by humans alone. But how compelling are these AI-generated ideas, and how can we improve their quality? Here, we introduce SciMuse, which uses 58 million research papers and a large-language model to generate research ideas. We conduct a large-scale evaluation in which over 100 research group leaders -- from natural sciences to humanities -- ranked more than 4,400 personalized ideas based on their interest. This data allows us to predict research interest using (1) supervised neural networks trained on human evaluations, and (2) unsupervised zero-shot ranking with large-language models. Our results demonstrate how future systems can help generating compelling research ideas and foster unforeseen interdisciplinary collaborations.

cs.AI↗

arXiv2024

Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics

Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive Virtual Reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way - as a human-in-the-loop - to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger-Horne-Zeilinger-state analyzer. Our results show the potential of VR for increasing a human researcher's ability to derive knowledge from graph-based generative AI that, which is a common abstract data representation used in diverse fields of science.

cs.HC↗

arXiv2024

Forecasting high-impact research topics via machine learning on evolving knowledge graphs

The exponential growth in scientific publications poses a severe challenge for human researchers. It forces attention to more narrow sub-fields, which makes it challenging to discover new impactful research ideas and collaborations outside one's own field. While there are ways to predict a scientific paper's future citation counts, they need the research to be finished and the paper written, usually assessing impact long after the idea was conceived. Here we show how to predict the impact of onsets of ideas that have never been published by researchers. For that, we developed a large evolving knowledge graph built from more than 21 million scientific papers. It combines a semantic network created from the content of the papers and an impact network created from the historic citations of papers. Using machine learning, we can predict the dynamic of the evolving network into the future with high accuracy (AUC values beyond 0.9 for most experiments), and thereby the impact of new research directions. We envision that the ability to predict the impact of new ideas will be a crucial component of future artificial muses that can inspire new impactful and interesting scientific ideas.

cs.DL↗