10 citations · 16 across the 7 of their papers we have counts for
7 papers · 1 filter
Near-Optimal Quantum Time Evolution Circuits via Provably Convergent Compression
Erenay Karacan, Isabel Nha Minh Le, Matteo D'Anna +3
Variational compression can significantly lower implementation overheads for encoding the time evolution of Hamiltonians into quantum circuits. However, they usually lack global co…
Hessian-vector products for tensor networks via recursive tangent-state propagation
Isabel Nha Minh Le, Roeland Wiersema, Christian B. Mendl
Optimizing tensor networks with standard first-order methods often leads to slow convergence and entrapment in local minima. Although second-order optimization offers enhanced robu…
Improved Ising Meson Spectroscopy Simulation on a Noisy Digital Quantum Device
Hao-Ti Hung, Isabel Nha Minh Le, Johannes Knolle +1
The transverse-field Ising model serves as a paradigm for studying confinement and excitation spectra, particularly the emergence of symmetry near criticality. However, exper…
A Quantum Algorithm with Polylogarithmic Depth per Trotter Step for the Extended Hubbard Model
Yu Wang, Martina Nibbi, Maxine Luo +4
The extended Hubbard model on a two-dimensional lattice captures key physical phenomena, but its simulation remains challenging because long-range interactions give rise to a large…
High-Performance Contraction of Quantum Circuits for Riemannian Optimization
Fabian Putterer, Max M. Zumpe, Isabel Nha Minh Le +2
This work focuses on optimizing the gates of a quantum circuit with a given topology to approximate the unitary time evolution governed by a Hamiltonian. Recognizing that unitary m…
Riemannian quantum circuit optimization based on matrix product operators
Isabel Nha Minh Le, Shuo Sun, Christian B. Mendl
We significantly enhance the simulation accuracy of initial Trotter circuits for Hamiltonian simulation of quantum systems by integrating first-order Riemannian optimization with t…
Symmetry-invariant quantum machine learning force fields
Isabel Nha Minh Le, Oriel Kiss, Julian Schuhmacher +2
Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate…