Physics

Jacob Barnett, Perimeter Institute

Locality and Exceptional Points in Pseudo-Hermitian Physics

This talk discusses the role of non-Hermitian operators in fundamental and effective theories of physics. An implicit assumption of the tensor product model of locality is that the inner product factorizes with the tensor product. Quasi-Hermitian quantum frameworks can be used to lift this assumption while preserving the reality of spectra and unitarity. After characterizing local observable algebras and expectation values, I will examine Bell's inequality and its generalizations, the nonlocal games, in the setting of quasi-Hermitian theories. Pseudo-Hermitian operators characterize systems with time-reversal symmetry. These operators exhibit rich perturbative and symmetry-breaking properties that are unparalleled in the Hermitian regime. I will convey some geometric and topological aspects of these features, with emphasis placed on non-interacting many-body systems.
 

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

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 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