93 citations · 425 across the 18 of their papers we have counts for
18 papers
A Quantitative Study of Sustained Focus in Large Language Models via Repetitive Deterministic Prediction Tasks
Wanda Hou, Leon Zhou, Hong-Ye Hu +3
We investigate the performance of large language models (LLMs) on repetitive deterministic prediction tasks and study how the sequence accuracy rate (SAR) scales with output length…
Measurement-Induced Criticality is Tomographically Optimal
Ahmed A. Akhtar, Hong-Ye Hu, Yi-Zhuang You
We develop a classical shadow tomography protocol utilizing the randomized measurement scheme based on hybrid quantum circuits, which consist of layers of two-qubit random unitary…
Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic
Andi Gu, Hong-Ye Hu, Di Luo +3
We introduce a quantum information theory-inspired method to improve the characterization of many-body Hamiltonians on near-term quantum devices. We design a new class of similarit…
Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning
Vincent Paul Su, ChunJun Cao, Hong-Ye Hu +3
The recently introduced Quantum Lego framework provides a powerful method for generating complex quantum error correcting codes (QECCs) out of simple ones. We gamify this process a…
Interpretable Quantum Advantage in Neural Sequence Learning
Eric R. Anschuetz, Hong-Ye Hu, Jin-Long Huang +1
Quantum neural networks have been widely studied in recent years, given their potential practical utility and recent results regarding their ability to efficiently express certain…
Scalable and Flexible Classical Shadow Tomography with Tensor Networks
Ahmed A. Akhtar, Hong-Ye Hu, Yi-Zhuang You
Classical shadow tomography is a powerful randomized measurement protocol for predicting many properties of a quantum state with few measurements. Two classical shadow protocols ha…
Logical shadow tomography: Efficient estimation of error-mitigated observables
Hong-Ye Hu, Ryan LaRose, Yi-Zhuang You +2
We introduce a technique to estimate error-mitigated expectation values on noisy quantum computers. Our technique performs shadow tomography on a logical state to produce a memory-…
Differentiable Programming of Isometric Tensor Networks
Chenhua Geng, Hong-Ye Hu, Yijian Zou
Differentiable programming is a new programming paradigm which enables large scale optimization through automatic calculation of gradients also known as auto-differentiation. This…
Classical Shadow Tomography with Locally Scrambled Quantum Dynamics
Hong-Ye Hu, Soonwon Choi, Yi-Zhuang You
We generalize the classical shadow tomography scheme to a broad class of finite-depth or finite-time local unitary ensembles, known as locally scrambled quantum dynamics, where the…
Hamiltonian-Driven Shadow Tomography of Quantum States
Hong-Ye Hu, Yi-Zhuang You
Classical shadow tomography provides an efficient method for predicting functions of an unknown quantum state from a few measurements of the state. It relies on a unitary channel t…
High-order Time-Reversal Symmetry Breaking Normal State
Meng Zeng, Lun-Hui Hu, Hong-Ye Hu +2
Spontaneous time-reversal symmetry breaking plays an important role in studying strongly correlated unconventional superconductors. When two superconducting gap functions with diff…
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
Hong-Ye Hu, Dian Wu, Yi-Zhuang You +2
Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse…
Topological and symmetry-enriched random quantum critical points
Carlos M. Duque, Hong-Ye Hu, Yi-Zhuang You +3
We study how symmetry can enrich strong-randomness quantum critical points and phases, and lead to robust topological edge modes coexisting with critical bulk fluctuations. These a…
Neural ODE and Holographic QCD
Koji Hashimoto, Hong-Ye Hu, Yi-Zhuang You
The neural ordinary differential equation (Neural ODE) is a novel machine learning architecture whose weights are smooth functions of the continuous depth. We apply the Neural ODE…
Quantum Magnetism in Wannier-Obstructed Mott Insulators
Xiao-Yang Huang, Taige Wang, Shang Liu +2
We develop a strong coupling approach towards quantum magnetism in Mott insulators for Wannier obstructed bands. Despite the lack of Wannier orbitals, electrons can still singly oc…
Resonant Quantum Search with Monitor Qubits
Frank Wilczek, Hong-Ye Hu, Biao Wu
We present an algorithm for the generalized search problem (searching marked items among items) based on a continuous Hamiltonian and exploiting resonance. This resonant al…
Machine Learning Holographic Mapping by Neural Network Renormalization Group
Hong-Ye Hu, Shuo-Hui Li, Lei Wang +1
The exact holographic mapping (EHM) provides an explicit duality map between a conformal field theory (CFT) configuration and a massive field propagating on an emergent classical g…
Optimizing Quantum Adiabatic Algorithm
Hongye Hu, Biao Wu
In quantum adiabatic algorithm, as the adiabatic parameter changes slowly from zero to one with finite rate, a transition to excited states inevitably occurs and this induce…