29 citations · 84 across the 20 of their papers we have counts for
18 papers · 1 filter
Covariant Approximate Quantum Codes for Protected Analog Computation
Mariia Elovenkova, Hong-Ye Hu, Susanne F. Yelin
Quantum error correction compatible with continuous symmetries is a fundamental problem in quantum information and a possible route to robust analog quantum simulation. Because the…
Provably Efficient Self-Calibrating Quantum Fault Tolerance
Weiyuan Gong, Hong-Ye Hu
Quantum error correction protects logical information only when every physical operation remains below the fault-tolerance threshold, a condition that must be maintained continuous…
Generative Learning for Quantum Measurement Design
Jun Dai, Olivier Nahman-Lévesque, Guillaume Rabusseau +2
Extracting quantum information from a quantum state is a fundamental task of quantum computation, often requiring the estimation of many non-commuting observables under a finite me…
Fast and Parallel High-Rate STAR Architecture for Megaquop Quantum Simulation
Refaat Ismail, Milan Kornjača, Hong-Ye Hu +4
Fault-tolerant quantum simulation is approaching a phase where encoding overhead, logical Clifford operations, magic-state preparation, and rotation synthesis must be optimized tog…
Learning Arbitrary Lindbladians with Quantum Error Correction
Nikita Romanov, Petr Ivashkov, Weiyuan Gong +4
We study ansatz-free Lindbladian learning, the problem of reconstructing the generator of an open quantum system without prior knowledge of its Hamiltonian or dissipator structures…
Opportunities and challenges in scaling quantum error detection on hardware
Yanis Le Fur, Ethan Egger, Hong-Ye Hu +3
Quantum error detection can produce unbiased expectation values that exponentially converge to noiseless results as the code distance is increased. Despite this, its performance as…
Entanglement and information scrambling in long-range measurement-only circuits
Abigail McClain Gomez, Fiona Abney-McPeek, Hong-Ye Hu +2
Measurement-only circuits provide a minimal setting in which repeated local projections can either generate or suppress many-body entanglement, giving rise to measurement-induced p…
Ansatz-Free Learning of Lindbladian Dynamics In Situ
Petr Ivashkov, Nikita Romanov, Weiyuan Gong +3
Characterizing the dynamics of open quantum systems at the level of microscopic interactions and error mechanisms is essential for calibrating quantum hardware, designing robust si…
Universal Dynamics with Globally Controlled Analog Quantum Simulators
Hong-Ye Hu, Abigail McClain Gomez, Liyuan Chen +6
Analog quantum simulators with global control fields have emerged as powerful platforms for exploring complex quantum phenomena. Despite these advances, a fundamental theoretical q…
Ansatz-free Hamiltonian learning with Heisenberg-limited scaling
Hong-Ye Hu, Muzhou Ma, Weiyuan Gong +4
Learning the unknown interactions that govern a quantum system is crucial for quantum information processing, device benchmarking, and quantum sensing. The problem, known as Hamilt…
Derandomized shallow shadows: Efficient Pauli learning with bounded-depth circuits
Katherine Van Kirk, Christian Kokail, Jonathan Kunjummen +7
Efficiently estimating large numbers of non-commuting observables is an important subroutine of many quantum science tasks. We present the derandomized shallow shadows (DSS) algori…
Efficient Measurement-Driven Eigenenergy Estimation with Classical Shadows
Yizhi Shen, Alex Buzali, Hong-Ye Hu +4
Quantum algorithms exploiting real-time evolution under a target Hamiltonian have demonstrated remarkable efficiency in extracting key spectral information. However, the broader po…
Circumventing Traps in Analog Quantum Machine Learning Algorithms Through Co-Design
Rodrigo Araiza Bravo, Jorge Garcia Ponce, Hong-ye Hu +1
Quantum machine learning QML algorithms promise to deliver near-term, applicable quantum computation on noisy, intermediate-scale systems. While most of these algorithms leverage q…
Large-scale quantum reservoir learning with an analog quantum computer
Milan Kornjača, Hong-Ye Hu, Chen Zhao +50
Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite t…
Low-Overhead Transversal Fault Tolerance for Universal Quantum Computation
Hengyun Zhou, Chen Zhao, Madelyn Cain +7
Fast, reliable logical operations are essential for realizing useful quantum computers. By redundantly encoding logical qubits into many physical qubits and using syndrome measurem…
Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows
Hong-Ye Hu, Andi Gu, Swarnadeep Majumder +7
Extracting information efficiently from quantum systems is a major component of quantum information processing tasks. Randomized measurements, or classical shadows, enable predicti…
Digital-analog quantum learning on Rydberg atom arrays
Jonathan Z. Lu, Lucy Jiao, Kristina Wolinski +6
We propose hybrid digital-analog learning algorithms on Rydberg atom arrays, combining the potentially practical utility and near-term realizability of quantum learning with the ra…
Predicting Arbitrary State Properties from Single Hamiltonian Quench Dynamics
Zhenhuan Liu, Zihan Hao, Hong-Ye Hu
Analog quantum simulation is an essential routine for quantum computing and plays a crucial role in studying quantum many-body physics. Typically, the quantum evolution of an analo…