3 citations · 6 across the 12 of their papers we have counts for
14 papers
Shallow Quantum Circuits for Geometrically Local Lindblad Dynamics
Zheyu Shen, Yusen Wu, Xiao Yuan +1
Open-system dynamics govern decoherence, transport, and engineered dissipation in quantum many-body systems. We propose a quantum algorithm for simulating time-independent, finite-…
Breaking the Multiplicative Overhead in Quantum Entropy Estimation
Junxiang Huang, Chenyang Li, Lu-Fan Zhang +2
The von Neumann, Tsallis, and Rényi entropies are fundamental measures of quantum information. A common estimation strategy multiplies the worst-case costs of spectral transformati…
From Block-encoding to Generalized Quantum Signal Processing: Principles, Algorithms and Applications
Tal Gurfinkel, Kaushika De Silva, Anuradha Mahasinghe +9
Modern quantum algorithms are increasingly formulated as coherent procedures for implementing polynomial transformations of operators and singular values. This perspective provides…
Efficient quantum state preparation on Quantinuum hardware
Archie Butterworth, Josh Green, Yusen Wu +2
Preparation and verification of specific quantum states is an important capability for quantum devices to realise advantages over classical computations and algorithms. In this wor…
Quantum Advantage with Adaptive Shallow Circuits
Yusen Wu, Yukun Zhang, XIaoming Zhang +2
Quantum advantage is widely expected to require sufficiently deep circuits, where correlations and global computational structure can grow beyond the reach of efficient classical s…
Efficient Lindbladian Learning from Constant-Time Pauli Responses
Jiaxing Song, Yukun Zhang, Xiao Yuan +1
Learning the generator of an open many-body system is more challenging than Hamiltonian learning: local responses, which can directly reveal coherent interaction terms in closed-sy…
CTQWformer: A CTQW-based Transformer for Graph Classification
Zhan Li, Wuqing Yu, Yusen Wu +1
Graph Neural Networks (GNN) and Transformer-based architectures have achieved remarkable progress in graph learning, yet they still struggle to capture both global structural depen…
Efficient Noisy Quantum State and Process Tomography
Chenyang Li, Shengxin Zhuang, Yukun Zhang +4
Efficiently characterizing large quantum states and processes is a central yet notoriously challenging task in quantum information science, as conventional tomography methods typic…
Heisenberg-Limited Quantum Eigenvalue Estimation for Non-normal Matrices
Yukun Zhang, Yusen Wu, Xiao Yuan
Estimating the eigenvalues of non-normal matrices is a foundational problem with far-reaching implications, from modeling non-Hermitian quantum systems to analyzing complex fluid d…
Measuring Less to Learn More: Quadratic Speedup in learning Nonlinear Properties of Quantum Density Matrices
Yukun Zhang, Yusen Wu, You Zhou +1
A fundamental task in quantum information science is to measure nonlinear functionals of quantum states, such as . Intuitively, one expects that computing a …
Hamiltonian Dynamics Learning: A Scalable Approach to Quantum Process Characterization
Yusen Wu, Yukun Zhang, Chuan Wang +1
Quantum process characterization is a fundamental task in quantum information processing, yet conventional methods, such as quantum process tomography, require prohibitive resource…
A Dequantized Algorithm for the Guided Local Hamiltonian Problem
Yukun Zhang, Yusen Wu, Xiao Yuan
The local Hamiltonian (LH) problem, the quantum analog of the classical constraint satisfaction problem, is a cornerstone of quantum computation and complexity theory. It is known…
Classical Algorithms for Hamiltonian Dynamics Mean Value and Guided Local Hamiltonian Problem
Yusen Wu, Yukun Zhang, Chuan Wang +1
The efficient simulation of quantum dynamics and ground states is a central challenge in physics and a key frontier for quantum advantage. While short-time evolution in one-dimensi…
Non-Hemolytic Peptide Classification Using A Quantum Support Vector Machine
Shengxin Zhuang, John Tanner, Yusen Wu +7
Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a…