250 citations · 1.3k across the 16 of their papers we have counts for
17 papers
Hamiltonian Learning and Certification via Eigenphase Engineering
Myeongjin Shin, Yu Tong
We study Hamiltonian learning for -local Hamiltonians at large step size, using only forward time evolution at integer multiples of a fixed time interval. For this task, our new…
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…
Robust ground-state energy estimation under depolarizing noise
Zhiyan Ding, Yulong Dong, Yu Tong +1
We present a novel ground-state energy estimation algorithm that is robust under global depolarizing error channels. Building upon the recently developed Quantum Exponential Least…
On the complexity of implementing Trotter steps
Guang Hao Low, Yuan Su, Yu Tong +1
Quantum dynamics can be simulated on a quantum computer by exponentiating elementary terms from the Hamiltonian in a sequential manner. However, such an implementation of Trotter s…
Learning many-body Hamiltonians with Heisenberg-limited scaling
Hsin-Yuan Huang, Yu Tong, Di Fang +1
Learning a many-body Hamiltonian from its dynamics is a fundamental problem in physics. In this work, we propose the first algorithm to achieve the Heisenberg limit for learning an…
Time-marching based quantum solvers for time-dependent linear differential equations
Di Fang, Lin Lin, Yu Tong
The time-marching strategy, which propagates the solution from one time step to the next, is a natural strategy for solving time-dependent differential equations on classical compu…
Is there evidence for exponential quantum advantage in quantum chemistry?
Seunghoon Lee, Joonho Lee, Huanchen Zhai +15
The idea to use quantum mechanical devices to simulate other quantum systems is commonly ascribed to Feynman. Since the original suggestion, concrete proposals have appeared for si…
Ground state preparation and energy estimation on early fault-tolerant quantum computers via quantum eigenvalue transformation of unitary matrices
Yulong Dong, Lin Lin, Yu Tong
Under suitable assumptions, the algorithms in [Lin, Tong, Quantum 2020] can estimate the ground state energy and prepare the ground state of a quantum Hamiltonian with near-optimal…
Entanglement area law for 1D gauge theories and bosonic systems
Nilin Abrahamsen, Yu Tong, Ning Bao +2
We prove an entanglement area law for a class of 1D quantum systems involving infinite-dimensional local Hilbert spaces. This class of quantum systems include bosonic models such a…
Provably accurate simulation of gauge theories and bosonic systems
Yu Tong, Victor V. Albert, Jarrod R. McClean +2
Quantum many-body systems involving bosonic modes or gauge fields have infinite-dimensional local Hilbert spaces which must be truncated to perform simulations of real-time dynamic…
Heisenberg-limited ground state energy estimation for early fault-tolerant quantum computers
Lin Lin, Yu Tong
Under suitable assumptions, the quantum phase estimation (QPE) algorithm is able to achieve Heisenberg-limited precision scaling in estimating the ground state energy. However, QPE…
Fast inversion, preconditioned quantum linear system solvers, and fast evaluation of matrix functions
Yu Tong, Dong An, Nathan Wiebe +1
Preconditioning is the most widely used and effective way for treating ill-conditioned linear systems in the context of classical iterative linear system solvers. We introduce a qu…
Enhancing robustness and efficiency of density matrix embedding theory via semidefinite programming and local correlation potential fitting
Xiaojie Wu, Michael Lindsey, Tiangang Zhou +2
Density matrix embedding theory (DMET) is a powerful quantum embedding method for solving strongly correlated quantum systems. Theoretically, the performance of a quantum embedding…
Near-optimal ground state preparation
Lin Lin, Yu Tong
Preparing the ground state of a given Hamiltonian and estimating its ground energy are important but computationally hard tasks. However, given some additional information, these p…
Optimal polynomial based quantum eigenstate filtering with application to solving quantum linear systems
Lin Lin, Yu Tong
We present a quantum eigenstate filtering algorithm based on quantum signal processing (QSP) and minimax polynomials. The algorithm allows us to efficiently prepare a target eigens…
Low-rank representation of tensor network operators with long-range pairwise interactions
Lin Lin, Yu Tong
Tensor network operators, such as the matrix product operator (MPO) and the projected entangled-pair operator (PEPO), can provide efficient representation of certain linear operato…
Projected Density Matrix Embedding Theory with Applications to the Two-Dimensional Hubbard Model
Xiaojie Wu, Zhi-Hao Cui, Yu Tong +3
Density matrix embedding theory (DMET) is a quantum embedding theory for strongly correlated systems. From a computational perspective, one bottleneck in DMET is the optimization o…