58 citations · 177 across the 18 of their papers we have counts for
21 papers
Size-independent Logarithmic Sobolev Inequalities for Quantum Gibbs Samplers
Hongrui Chen, Zhiyan Ding, Jianfeng Lu
We propose a new method for proving and modified logarithmic Sobolev inequalities with constants independent of system size for quantum Gibbs samplers whose Lindblad generato…
Physical limits to stochastic classifiers
Timur Aslyamov, Massimiliano Esposito
Stochastic physical computers use random transitions between states to process inputs. We quantify their ability to solve distinct tasks as the control parameters of a device vary.…
Spectral Analysis Beyond the Spectral Gap: Rapid Mixing of Quantum Gibbs Samplers in One Dimension
Hongrui Chen, Zhiyan Ding
We prove rapid mixing of a quantum Gibbs sampler for general one-dimensional non-commuting local spin systems at every fixed positive temperature. In particular, for a system of $n…
Quantum Algorithms for Nonlinear Differential Equations via Pivot-Shifted Carleman Linearization
Ke Wang, Zikang Jia, Shravan Veerapaneni +1
We develop a pivot-shifted Carleman linearization framework for quantum algorithms solving quadratic nonlinear ordinary differential equations. By shifting the dynamics by a pivot…
Overcoming the Lamb Shift in System-Bath Interaction Models via KMS Detailed Balance: High-Accuracy Thermalization with Time-Bounded Interactions
Hongrui Chen, Zhiyan Ding, Ruizhe Zhang
We investigate quantum thermal state preparation algorithms based on system-bath interactions and uncover a surprising phenomenon in the weak-coupling regime. We rigorously prove t…
Provably Efficient Long-Time Exponential Decompositions of Non-Markovian Gaussian Baths
Zhen Huang, Zhiyan Ding, Ke Wang +3
Gaussian baths are widely used to model non-Markovian environments, yet the cost of accurate simulation at long times remains poorly understood, especially when spectral densities…
Uniform-in-time propagation of chaos for consensus-based minimax algorithm
Erhan Bayraktar, Zhiyan Ding, Ibrahim Ekren +1
We study the large-population convergence of a consensus-based algorithm for the saddle point problem proposed by ArXiv: 2212.12334, establishing the uniform-in-time propagation of…
Beyond Lindblad Approximation: Rigorous Guarantees for Thermal and Ground State Preservation under System-Bath Interactions
Ke Wang, Zhiyan Ding
We establish rigorous guarantees for quantum thermal and ground state preparation via system-bath interactions beyond the Lindblad approximation regime. In contrast to existing ana…
Quantum Replica Exchange
Zherui Chen, Joao Basso, Zhiyan Ding +1
The presence of energy barriers in the state space of a physical system can lead to exponentially slow convergence for sampling algorithms like Markov chain Monte Carlo (MCMC). In…
Quantum Filtering and Analysis of Multiplicities in Eigenvalue Spectra
Zhiyan Ding, Lin Lin, Yilun Yang +1
Fine-grained spectral properties of quantum Hamiltonians, including both eigenvalues and their multiplicities, provide useful information for characterizing many-body quantum syste…
Simple and efficient end-to-end quantum thermal and ground state preparation
Zhiyan Ding, Yongtao Zhan, John Preskill +1
Quantum computing algorithms for many-body physics, chemistry and materials science typically require the preparation of thermal or ground states for a given Hamiltonian. We propos…
Operator-Level Quantum Acceleration of Non-Logconcave Sampling
Jiaqi Leng, Zhiyan Ding, Zherui Chen +1
Sampling from probability distributions of the form , where is a continuous potential, is a fundamental task across physics, chemistry, biology, computer scie…
Rapid quantum ground state preparation via dissipative dynamics
Yongtao Zhan, Zhiyan Ding, Jakob Huhn +4
Inspired by natural cooling processes, dissipation has become a promising approach for preparing low-energy states of quantum systems. However, the potential of dissipative protoco…
Polynomial-Time Preparation of Low-Temperature Gibbs States for 2D Toric Code
Zhiyan Ding, Zeph Landau, Bowen Li +2
We propose a polynomial-time algorithm for preparing the Gibbs state of the two-dimensional toric code Hamiltonian at any temperature, starting from any initial condition, signific…
Rapid initial state preparation for the quantum simulation of strongly correlated molecules
Dominic W. Berry, Yu Tong, Tanuj Khattar +8
Studies on quantum algorithms for ground state energy estimation often assume perfect ground state preparation; however, in reality the initial state will have imperfect overlap wi…
Lower bound for simulation cost of open quantum systems: Lipschitz continuity approach
Zhiyan Ding, Marius Junge, Philipp Schleich +1
Simulating quantum dynamics is one of the most promising applications of quantum computers. While the upper bound of the simulation cost has been extensively studied through variou…
Efficient quantum Gibbs samplers with Kubo--Martin--Schwinger detailed balance condition
Zhiyan Ding, Bowen Li, Lin Lin
Lindblad dynamics and other open-system dynamics provide a promising path towards efficient Gibbs sampling on quantum computers. In these proposals, the Lindbladian is obtained via…
The ESPRIT algorithm under high noise: Optimal error scaling and noisy super-resolution
Zhiyan Ding, Ethan N. Epperly, Lin Lin +1
Subspace-based signal processing techniques, such as the Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT) algorithm, are popular methods for spectral es…
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…
Simulating Open Quantum Systems Using Hamiltonian Simulations
Zhiyan Ding, Xiantao Li, Lin Lin
We present a novel method to simulate the Lindblad equation, drawing on the relationship between Lindblad dynamics, stochastic differential equations, and Hamiltonian simulations.…
Random coordinate descent: a simple alternative for optimizing parameterized quantum circuits
Zhiyan Ding, Taehee Ko, Jiahao Yao +2
Variational quantum algorithms rely on the optimization of parameterized quantum circuits in noisy settings. The commonly used back-propagation procedure in classical machine learn…