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The recent advancement of machine learning provides new opportunities for tackling challenges in quantum science, ranging from condensed matter physics, high energy physics to quantum information science. In this talk, I will first discuss a class of anti-symmetric wave functions based on neural network backflow,  which is efficient for simulating strongly-correlated lattice models and artificial quantum materials. Next, I will talk about recent progress of simulating continuum quantum field theories with neural quantum field state, and lattice gauge theories such as 2+1D quantum electrodynamics with finite density dynamical fermions using gauge symmetric neural networks. I will further discuss neural network representation based on positive-value-operator and phase space measurements for quantum dynamics simulations. Finally, I will present applications of machine learning in quantum control, quantum optimization and quantum machine learning.

Zoom link:  https://pitp.zoom.us/j/93834456412?pwd=R0hxdEpxanFFRnZmTHlqZTBXRi82QT09

In this colloquium, I will review how the notion of quantum error-correction has transformed our understanding of quantum gravity in the past decade.

Zoom link:  https://pitp.zoom.us/j/94349082665?pwd=OFdJdkpHU1NEcmlNeW5pWlg4WmNBZz09

 I will present a novel behavior developed by scattering amplitudes in certain regions of the kinematic space, in which amplitudes factorize into 3 pieces without becoming singular. This is opposed to what happens in standard factorization or soft limits. We call this behavior a 3-split, and it is "semi-local" in nature. As 3-splits cannot be obtained from standard unitarity arguments, they represent a new phenomenon in QFT. I will introduce 3-splits in their simplest version, i.e. for the biadjoint scalar theory (which I will also introduce). If time allows, I will also comment on how 3-splits arise in other QFTs and how they lead to the discovery of more general theories.

Zoom link:  https://pitp.zoom.us/j/95575818902?pwd=V1RKYW9WMmZQNElVL2VyUGFGMFgxQT09

In quantum many-body physics, we aim to understand and predict the exciting phenomena emerging from the interactions of many quantum particles. In this talk I will use the paradigmatic Fermi-Hubbard model, a conceptually simple model of interacting fermionic particles, as an example to highlight different approaches to gain insights into quantum many-body problems. I will describe a semi-analytical theory, established numerical methods, machine learning techniques, as well as quantum simulation experiments, which provide detailed information about the quantum state of the system. A particular focus will be on how to combine these different approaches to learn as much as possible about the system of interest, for example through new observables and modified microscopic models.

Zoom link:  https://pitp.zoom.us/j/94368447256?pwd=clhQcUJNaTR0UjVydnBHelo4N3JIQT09

Generative modeling via diffusion processes is already a vast field of literature. In this introduction, I will give an entry point to this field by going over the main concepts and deriving the essential results of the area. Thus, by the end of the talk, we would have a minimal pipeline for implementing the generative model. Furthermore, I will outline several alternative ways for learning such models that the community has developed in recent years. These directions bring novel perspectives and new capabilities of generative modeling.

Zoom link:  https://pitp.zoom.us/j/98786491081?pwd=U1cvZzBQT2VUZDl5Ykd0c1lqY29aZz09

Recently, machine learning has become a popular tool to use in fundamental science, including lattice field theory. Here I will report on some recent progress, including the Inverse Renormalisation Group and quantum-field theoretical machine learning, combining insights of lattice field theory and machine learning in a hopefully constructive manner.

Zoom link:  https://pitp.zoom.us/j/95456375462?pwd=WmtZMloyclAyZzBwVEZHQ3gxVnkrUT09

In 2008 Masahiro Hotta proposed a protocol for transporting energy between two localized observers A and B without any energy propagating from A to B. When this protocol is applied to the vacuum state of a quantum field, the local energy density in the field achieves negative values, violating energy conditions

We will explore the protocol of quantum energy teleportation and show how quantum information techniques can be used to activate thermodynamically passive states. We will review the first experiment showcasing the local activation of ground state energy (carried out in 2022), and we will discuss the potential of this relativistic quantum information protocol to create exotic distributions of stress-energy density in a quantum field theory, and how spacetime might react to them.

Zoom link:  https://pitp.zoom.us/j/98393523926?pwd=LzI4N1UyLzR4QVVGcENEbjBycjJwUT09

In recent years, machine learning has been successfully used to identify phase transitions and classify phases of matter in a data-driven manner. Neural network (NN)-based approaches are particularly appealing due to the ability of NNs to learn arbitrary functions. However, the larger an NN, the more computational resources are needed to train it, and the more difficult it is to understand its decision making. Thus, we still understand little about the working principle of such machine learning approaches, when they fail or succeed, and how they differ from traditional approaches. In this talk, I will present analytical expressions for the optimal predictions of three popular NN-based methods for detecting phase transitions that rely on solving classification and regression tasks using supervised learning at their core. These predictions are optimal in the sense that they minimize the target loss function. Therefore, in practice, optimal predictive models are well approximated by high-capacity predictive models, such as large NNs after ideal training. I will show that the analytical expressions we have derived provide a deeper understanding of a variety of previous NN-based studies and enable a more efficient numerical routine for detecting phase transitions from data.

Zoom Link: https://pitp.zoom.us/j/91642481966?pwd=alkrWEFFcFBvRlJEbDRBZWV3MFFDUT09