45 citations · 111 across the 12 of their papers we have counts for
14 papers
Optimal Linear Combination of Hamiltonian Simulation for Dissipative Dynamics
Hongkang Ni, Lexing Ying
Linear combination of Hamiltonian simulation (LCHS) is an efficient method for simulating dissipative dynamics on quantum computers. The choice of the LCHS kernel controls the cost…
Quantum Eigenvalue Transformation via Linear Combination of Hamiltonian Simulation: A Weyl Calculus Approach
Hongkang Ni, Lexing Ying
Linear combination of Hamiltonian simulation (LCHS) provides an efficient method for implementing matrix exponentials on quantum computers. In this paper, we develop LCHS…
Inverse nonlinear fast Fourier transform on SU(2) with applications to quantum signal processing
Hongkang Ni, Rahul Sarkar, Lexing Ying +1
The nonlinear Fourier transform (NLFT) extends the classical Fourier transform by replacing addition with matrix multiplication. While the NLFT on has been widel…
Fast Phase Factor Finding for Quantum Signal Processing
Hongkang Ni, Lexing Ying
This paper presents two efficient and stable algorithms for recovering phase factors in quantum signal processing (QSP), a crucial component of many quantum algorithms. The first a…
Quantum wave packet transforms with compact frequency support
Hongkang Ni, Lexing Ying
Different kinds of wave packet transforms are widely used for extracting multi-scale structures in signal processing tasks. This paper introduces the quantum circuit implementation…
Quantum Multiple Eigenvalue Gaussian filtered Search: an efficient and versatile quantum phase estimation method
Zhiyan Ding, Haoya Li, Lin Lin +3
Quantum phase estimation is one of the most powerful quantum primitives. This work proposes a new approach for the problem of multiple eigenvalue estimation: Quantum Multiple Eigen…
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…
Robust iterative method for symmetric quantum signal processing in all parameter regimes
Yulong Dong, Lin Lin, Hongkang Ni +1
This paper addresses the problem of solving nonlinear systems in the context of symmetric quantum signal processing (QSP), a powerful technique for implementing matrix functions on…
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…
Infinite quantum signal processing
Yulong Dong, Lin Lin, Hongkang Ni +1
Quantum signal processing (QSP) represents a real scalar polynomial of degree using a product of unitary matrices of size , parameterized by real numbers cal…