Research

We develop quantum algorithms for simulating lattice gauge theories on near-term and early fault-tolerant devices, exploiting the physical structure of the target Hamiltonians to improve algorithmic performance and resource efficiency. Our current research directions include:

Variational Quantum Algorithms 

Development of tailored ansatz circuits for computing ground states of 2+1D lattice gauge theories using the Variational Quantum Eigensolver (VQE), with a focus on adaptive construction strategies (ADAPT-VQE), symmetry-informed operator pools, and convergence analysis.

Sampling-based Krylov Quantum Diagonalization 

Study of quantum algorithms based on real-time evolution for sampling Krylov subspaces, enabling efficient ground-state approximation within reduced Hilbert spaces.

Qudit Architectures

Exploratory research on qudit-based quantum computing architectures for lattice gauge theory simulations, which offer a promising route toward more resource-efficient and accurate simulations of higher-dimensional systems.

We develop and apply tensor network methods to study lattice field theories and strongly coupled quantum many-body systems, targeting physical regimes beyond the reach of conventional Monte Carlo approaches. Our current research directions include:

Tree Tensor Networks 

Application of exact diagonalization and tree tensor network methods to lattice gauge theories in (3+1) dimensions, with the aim of simulating topological terms and exploring the resulting phase structure.

Matrix Product States

Computation of the step-scaling function in the Hamiltonian formulation of 2+1D U(1) gauge theory using matrix product states.

Entanglement Measures

Investigation of entanglement measures in strongly interacting quantum many-body systems and quantum field theories, using a combination of Monte Carlo simulations and tensor network techniques.

We develop and apply machine-learning methods to improve sampling and optimization in lattice field theories and strongly coupled quantum many-body systems. Our current research directions include:

(Stochastic) Normalizing Flows and Non-equilibrium Monte Carlo 

Development of machine-learning-based methods for ergodic simulation of strongly coupled lattice systems, including the Hubbard model and (non)Abelian gauge theories, using normalizing flows, stochastic normalizing flows, non-equilibrium Monte Carlo simulations, and annealing-based techniques.

Density of States with Normalizing Flows

Investigation of the density of states method for lattice field theories with a sign problem, applied to the Hubbard model and U(1) gauge theory, using normalizing flows to reconstruct the generalized density of states.

Optimization Methods

Development of an Extreme Value Extension (EVE) to the Adaptive Moment Estimation (ADAM) optimizer, designed to improve robustness in highly ill-conditioned optimization problems arising in lattice field theory applications and beyond.

We study Quantum Chromodynamics on the lattice, with a focus on finite-density regimes and the development of improved computational methods. Our current research directions include:

Lattice QCD at Finite Density

Study of lattice QCD at finite chemical potential, addressing the sign problem via imaginary chemical potential and Lefschetz thimbles, with application to the QCD phase diagram.

Method Development for Lattice QCD

Development of methods for reconstructing spectral functions and transport coefficients from Euclidean correlators, as well as for flexible interpolation of lattice QCD observables across bare parameters using conditional masked autoregressive flows.