Talks

In this talk, I will discuss the projects I have done in my Ph.D. https://scholar.google.com/citations?user=SiMLZi8AAAAJ&hl=en. My research lies at the intersection of quantum many-body physics and quantum information, aiming to unravel the intrinsic complexity of many-body dynamics using both analytical methods and quantum informational approaches. In the first direction, we develop several analytical frameworks, including solvable quantum circuit dynamics, tensor-network states, and non-equilibrium field-theoretic calculations, to study both non-equilibrium physics and open-system dynamics. In particular, we proposed the concept of mixed state deep thermalization, a new paradigm that extends conventional thermalization to the ensemble of conditional mixed states. In the second direction, we investigate the quantum complexity of non-Markovian open many-body systems, establishing Lieb–Robinson bounds and determining the optimal digital-simulation resources required to simulate such dynamics.

Maximizing our chances of discovering new physics from next-generation cosmological surveys requires making robust theoretical predictions of the nonlinear processes governing both the early Universe and the late-time large-scale structure. In this talk, I will present advances in two complementary methods for constraining the physics of the early Universe with cosmological survey data: forward simulation from inflation to late-time observables, and machine-learning-assisted Bayesian reconstruction of primordial initial conditions. In the first approach, we study the early Universe by directly simulating its dynamics. I have developed the first code for simulating multifield inflationary theories on a discrete lattice with enough precision to robustly predict primordial non-Gaussianity in higher-order N-point correlation functions. Focusing on axion-U(1) inflation, we accurately characterize the rich phenomenology predicted by this model, including modifications to the power spectrum, bispectrum, and higher-order correlation functions that violate parity. Using the output of these inflation simulations as initial conditions for N-body simulations, we effectively simulate the entire history of the Universe from inflation to late-time large-scale structure, finally revealing what inflation actually predicts about the origin of structures in the Universe. In the second approach, we reconstruct the initial conditions of the Universe using Bayesian inference with a fast, accurate, differentiable model of large-scale structure formation. I developed the first field-level emulator for large-scale structure, training a convolutional neural network to map linear initial conditions directly to the late-time matter field as modelled by computationally expensive N-body simulations. The model accelerates predictions by a factor of 1000 over full N-body simulations and achieves percent-level accuracy at deeply nonlinear scales (1 Mpc), outperforming fast particle-mesh simulations. This field-level emulator opens the possibility of reconstructing the initial conditions of the Universe using Bayesian inference techniques that include information from late-time nonlinear scales, as I will demonstrate with an application to simulated data. This highly nontrivial task involved exploring a million-dimensional parameter space using Hamiltonian Monte Carlo sampling and was only possible due to the combined speed, accuracy, and differentiability of the emulator.