Diffusion models have emerged as powerful tools in generative modeling, especially in image generation tasks. In this talk, we introduce a novel perspective by formulating diffusion models using the path integral method introduced by Feynman for describing quantum mechanics. We find this formulation providing comprehensive descriptions of score-based diffusion generative models, such as the...
Quantum annealing offers a hardware route to solving rugged discrete optimisation problems that appear throughout AI. This talk shows how to cast learning and inference tasks into QUBO or Ising form, then use forward and reverse annealing to navigate nonconvex loss landscapes. I will present compact case studies in classifier training, feature selection, model selection, and physics parameter...
Modern physics rests on two pillars: quantum mechanics which governs the microscopic world and general relativity which describes gravity and the structure of spacetime. Yet, these two pillars are fundamentally incompatible. String theory provides a promising way forward in unifying quantum mechanics with gravity, but it comes at a price of having an enormous number of solutions. The vastness...
In this talk I discuss how understanding the observable consequences of quantum gravity, in particular string theory models, is accelerated using AI methods. This overview will highlight several examples of using physics inspired neural networks to solve Einsteins equations in higher dimensions, differentiable programming to find solutions to string theory equations of motion, and how...
Modern machine learning and artificial intelligence fundamentally change how we analyze huge volumes of data in particle physics and adjacent scientific disciplines. These breakthroughs promise new insights into major scientific questions such as the nature of dark matter or the existence of physical phenomena beyond the standard model. This talk will provide an overview of recent, exciting...
At the centennial of quantum mechanics, I will survey the interface of AI × quantum physics with symmetry as the guiding theme. First, I will present work on the Ising model, which also marks its centennial, showing in the two-dimensional case that a convolutional neural network can extract phase transition signals and estimate the critical point without prior knowledge of the order parameter....
The talk illustrates how a generative AI model can be trained to learn the relationship between geometry and quantum field theory, producing Type IIB brane configurations in string theory that realize these field theories and tracking variations of these brane configurations that distinguish gauge theory phases related by duality. We focus on a particular family of 4‑dimensional supersymmetric...
High-energy colliders, such as the Large Hadron Collider (LHC) at CERN, are genuine quantum machines by nature, and thus, following Richard Feynman’s original motivation for quantum computing, the scattering processes occurring there should be more effectively simulated by a quantum system. While the dream of a fully-fledged quantum event generator for simulating scattering processes at...
Machine learning and deep learning have revolutionized computational physics, particularly in the simulation of complex systems. Equivariance plays a crucial role in modeling physical systems, as it enforces symmetry constraints that act as strong inductive biases on the learned probability distributions. However, incorporating such symmetries into models can sometimes lead to low acceptance...
While machine learning techniques are incredibly powerful, they are also notoriously difficult to interpret. This poses a problem fore research areas such as pure mathematics or certain fields in theoretical physics, which require rigor and understanding, while ML algorithms are often stochastic and black box. I will first give a brief overview of ML techniques that lead to rigorous, exact...
The evaluation of fixed-order perturbative QFT transition matrix elements forms the central component of simulations of scattering events at collider experiments as provided by Monte Carlo event generators. In view of the physics requirements of the LHC experiments high-multiplicity processes at high perturbative accuracy need to be addressed. This posses a severe challenge to the current...
A hundred years after the birth of quantum mechanics, we are entering its second revolution, the rise of quantum technology. A representative example is quantum computing, which performs computation based on the principles of quantum mechanics, and has already begun to demonstrate quantum advantage in certain physical simulations. In this talk, I will introduce our efforts to harness such...
According to the holographic principle, one of the most influential contemporary themes in physics, gravitational dynamics in the "bulk" spacetime is dual to the quantum physics of a system defined on its "boundary." We employ a deep learning approach to infer the bulk spacetime geometry from boundary quantum data, such as conductivity and entanglement entropy. In particular, we apply this...
Collisions of high energy particles trigger multiple interactions and result in complex patterns of particles in the final state. The resulting particle production patterns exhibit fascinating quantum phenomena such as spin correlation, color coherence and quantum entanglement. Theoretical particle physics has been evolving continuously to deepen our understanding of these phenomena, to...
We argue how AI can assist mathematics in three ways: theorem-proving, conjecture formulation, and language processing. Inspired by initial experiments in geometry and string theory in 2017, we summarize how this emerging field has grown over the past years, and show how various machine-learning algorithms can help with pattern detection across disciplines ranging from algebraic geometry to...
LHC physics, just like our lives, is being transformed by modern machine learning. This is motivated by the vast data stream and the role of simulations encoding fundamental physics knowledge. The scientific AI program around the LHC comes with unique advantages: we understand the feature space and scattering dynamics in terms of fundamental symmetries and quantum field theory; we have full...
We apply AI to the education domain, aiming to prepare high-quality digital materials for students—specifically transforming lectures from a hadron physics course into reliable resources. The core challenge is to combine several streams of information (speech, formulas, figures) into a coherent product, which cannot be solved by a single-prompt approach. Our pipeline employs embedding-based...
Designing effective quantum circuits is a central challenge in quantum computing, covering a wide range of tasks including constructing feature maps for quantum machine learning, designing ansätze for variational algorithms, and tailoring circuits to specific problem instances. Over the years, a variety of quantum circuits have been proposed through manual design and exploration. However, due...
We show that a family of birational transformations that relate toric Fano 3-folds defined by reflexive lattice polytopes can be identified with mass deformations of corresponding 2d (0,2) supersymmetric quiver gauge theories. These theories are realized by a Type IIA brane configuration known as brane brick models. We further show that the same family of birational transformations extends to...
Entanglement asymmetry is a measure that quantifies the degree of symmetry breaking at the level of a subsystem. In this work, we investigate the entanglement asymmetry in $\widehat{su}(N)_k$ Wess-Zumino-Witten model and discuss the quantum Mpemba effect for $\text{SU}(N)$ symmetry, the phenomenon that the more symmetry is initially broken, the faster it is restored. Due to the...
Compared to the $\Upsilon(4S)$, the $\Upsilon(5S)$ can decay into excited $B^{0*}$, giving rise to $B^0/\bar{B}^0$ pairs in different quantum states. Directly after the $\Upsilon(5S)$ decay, the produced $B^{0(*)}\bar{B}^{0(*)}$ pairs are expected to be in a $J^{PC} = 1^{--}$ state. Following the radiative transition $B^{0*} \to B^0 \gamma$, the system evolves into states with $J^{PC} =...
In Yang-Mills theory, which describes the interactions of elementary particles, gluon scattering amplitudes in the presence of an instanton play a crucial role in understanding the phenomenon of confinement and in theoretical calculations for accelerator experiments. It is known that, in the strong coupling and N→∞ limit of N=4 Super Yang-Mills (SYM) theory, the gluon scattering amplitude can...
Unfolding, for example of distortions imparted by detectors, provides suitable and publishable representations of LHC data. Many methods for unbinned and high-dimensional unfolding using machine learning based have been proposed, but no generative method has been shown to scale to the several hundred dimensions necessary to fully characterize LHC collisions. This project proposes a new...
We provide a simple toy model of machine learning that reconstructs QFT Lagrangians from the data of scattering amplitudes. We evaluate the performance differences that depend on the neural network architecture, and discuss the model's predictive ability for heavy particles that are not explicitly present in law energy scattering data.
Abstract
Understanding the particle nature of dark matter remains one of the key challenges in contemporary physics. Although the thermal relic picture provides a natural explanation for the observed dark matter density, the continuing absence of positive signals in direct detection experiments calls into question the simplest WIMP hypotheses. A compelling alternative scenario emerges when...
The phase diagram of QCD at finite densities remains numerically inaccessible by classical computations. Quantum computers, with their potential for exponential speedup, could overcome this challenge. However, their current physical implementations are affected by quantum noise. In this contribution, I will introduce a novel quantum error mitigation technique based on a general BBGKY-like...
We address the inverse problem in Type IIB flux compactifications of identifying flux vacua with targeted phenomenological properties such as specific superpotential values or tadpole constraints using conditional generative models. These machine learning techniques overcome computational bottlenecks in traditional approaches such as rejection sampling and Markov Chain Monte Carlo (MCMC),...