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The operator algebraic approach to quantum field theory is called algebraic quantum field theory. In this setting, we consider a family of operator algebras generated by observables in spacetime regions. This has been particularly successful in 2-dimensional conformal field theory. We present roles of tensor categories, modular invariance, classification theory, induction machinery and connections to vertex operator algebras.

After reviewing the motivation and challenges connected with the dRGT theory of ghost-free massive gravity, we discuss our recent progress in understanding non-linear dynamics of this model. In spherical symmetry, numerical studies suggest the formation of naked singularities during gravitational collapse of matter. Analytically, the same can be seen in the limit where the graviton mass is much smaller than the scales of the matter present. Both of these results underline the need to move beyond spherical symmetry to try and obtain realistic predictions. To that end, we present a new ‘harmonic-inspired’ formulation of the minimal model and argue that it is well-posed, opening the door to full 3+1 numerical simulations. 

I will review some recent progress in derived differential geometry, in particular pertaining to moduli stacks of solutions of elliptic partial differential equations on manifolds (with boundaries, and also with `logarithmic' boundaries, which include, for instance, manifolds with asymptotically cylindrical ends). In particular, this framework allows one to work efficiently with the compactified moduli spaces of symplectic topology and gauge theory. In another direction, I will explain some work in progress on the derived geometry of jet spaces, which can be used to endow moduli stacks of solutions of EOMs of a classical field theory with shifted symplectic structures.

Random unitaries form the backbone of numerous components of quantum technologies, and serve as indispensable toy models for complex processes in quantum many-body physics. In all of these applications, a crucial consideration is in what circuit depth a random unitary can be generated. I will present recent work, in which we show that local quantum circuits can form random unitaries in exponentially lower circuit depths than previously thought. We prove that random quantum circuits on any geometry, including a 1D line, can form approximate unitary designs over n qubits in log n depth. In a similar manner, we construct pseudorandom unitaries (PRUs) in 1D circuits in poly log n depth, and in all-to-all-connected circuits in poly log log n depth. These shallow quantum circuits have low complexity and create only short-range entanglement, yet are indistinguishable from unitaries with exponential complexity. Applications of our results include proving that classical shadows with 1D log-depth Clifford circuits are as powerful as those with deep circuits, demonstrating superpolynomial quantum advantage in learning low-complexity physical systems, and establishing quantum hardness for recognizing phases of matter with topological order.

UTe₂ is an intriguing recently discovered superconductor that exhibits a wide range of exotic properties. Experimental evidence increasingly supports spin-triplet pairing, and under pressure or in magnetic fields, UTe₂ displays multiple superconducting phases, including a remarkable reentrant phase above 40 T. However, conflicting results persist regarding the presence of chiral and time-reversal symmetry breaking. Recent STM measurements have identified a charge density wave in the normal state, which couples with the superconducting state at lower temperatures to form a pair density wave. In this talk, I will provide an overview of the latest developments in understanding the unconventional superconductivity of UTe₂.

Galaxies are the medium through which we study the structure of the universe. However, widely applied statistical models of galaxies are generally over-simplified: even recently proposed models cannot capture the dependencies on environment or formation history. To solve this problem, I will introduce Graph Neural Networks (GNNs), a general and ideal tool for physical modelling. Geometrically constrained GNNs vastly improve our models, and allow us to ask detailed questions about the importance of formation history and environment for cosmological galaxy modeling. I will also prove a surprising equivalence between these two aspects of galaxy formation.