45 citations · 138 across the 13 of their papers we have counts for
6 papers · 1 filter
Quantum Hamiltonian Learning for the Fermi-Hubbard Model
Hongkang Ni, Haoya Li, Lexing Ying
This work proposes a protocol for Fermionic Hamiltonian learning. For the Hubbard model defined on a bounded-degree graph, the Heisenberg-limited scaling is achieved while allowing…
Heisenberg-limited Hamiltonian learning for interacting bosons
Haoya Li, Yu Tong, Hongkang Ni +2
We develop a protocol for learning a class of interacting bosonic Hamiltonians from dynamics with Heisenberg-limited scaling. For Hamiltonians with an underlying bounded-degree gra…
A note on spike localization for line spectrum estimation
Haoya Li, Hongkang Ni, Lexing Ying
This note considers the problem of approximating the locations of dominant spikes for a probability measure from noisy spectrum measurements under the condition of residue signal,…
On adaptive low-depth quantum algorithms for robust multiple-phase estimation
Haoya Li, Hongkang Ni, Lexing Ying
This paper is an algorithmic study of quantum phase estimation with multiple eigenvalues. We present robust multiple-phase estimation (RMPE) algorithms with Heisenberg-limited scal…
On low-depth algorithms for quantum phase estimation
Hongkang Ni, Haoya Li, Lexing Ying
Quantum phase estimation is one of the critical building blocks of quantum computing. For early fault-tolerant quantum devices, it is desirable for a quantum phase estimation algor…
On efficient quantum block encoding of pseudo-differential operators
Haoya Li, Hongkang Ni, Lexing Ying
Block encoding lies at the core of many existing quantum algorithms. Meanwhile, efficient and explicit block encodings of dense operators are commonly acknowledged as a challenging…