Published in PRX Quantum, 2025
We demonstrate, via an analogy between optimal variational counterdiabatic protocols and polynomial fitting, that there are system-independent optimal coefficients for an expansion of the adiabatic gauge potential in Krylov space.
Published in Physical Review B, 2024
We systematically identify experimentally realistic modifications to annealing Hamiltonians where local control can prepare the ground state of annealing Hamiltonians with exponentially greater fidelity, and replicates adiabatic dynamics in shorter times.
Published in Condensed Matter, 2022
We explore the connection between the glassy and frustrated dynamics of the Newman-Moore model and difficulty in training an neural network to capture the ground state(s).
Published in Physical Review A, 2021
In this work, we propose a novel recurrent neural network ansatz which respects the U(1) symmetry of an underlying Hamiltonian, significantly improving the performance of neural network quantum state reconstruction.