22 citations · 23 across the 6 of their papers we have counts for
8 papers
Distilling Knowledge from Search: Decoder Model Factory for Quantum LDPC Codes
Anil Bilgin, Wanda Hou, Pranav Srinivas +1
We introduce GraphMP, a learning-based message-passing decoder for quantum low-density parity-check codes that transfers without fine-tuning across code families, code sizes, physi…
Persistent Memory and Iterative Improvement in an LLM Agent for Chinese-Character Drawing
Peilin Wu, Yi-Zhuang You
Does persistent memory accumulated from previous attempts help an LLM agent solve new problems, and does memory design affect that benefit? We study programmatic Chinese-character…
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…
Machine learning the effects of many quantum measurements
Wanda Hou, Samuel J. Garratt, Norhan M. Eassa +4
Measurements are essential for the processing and protection of information in quantum computers. They can also induce long-range entanglement between unmeasured qubits. However, w…
Measurement-Based Quantum Diffusion Models
Xinyu Liu, Jingze Zhuang, Wanda Hou +1
We introduce measurement-based quantum diffusion models that bridge classical and quantum diffusion theory through randomized weak measurements. The measurement-based approach natu…
Machine Learning Symmetry Discovery for Integrable Hamiltonian Dynamics
Wanda Hou, Molan Li, Yi-Zhuang You
We propose a data-driven Machine-Learning Symmetry Discovery (MLSD) framework for identifying continuous symmetry generators and their Lie-algebraic structure directly from phase-s…
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…
Sequential learning on a Tensor Network Born machine with Trainable Token Embedding
Wanda Hou, Miao Li, Yi-Zhuang You
Generative models aim to learn the probability distributions underlying data, enabling the generation of new, realistic samples. Quantum inspired generative models, such as Born ma…