8 citations · 24 across the 15 of their papers we have counts for
5 papers · 1 filter
Fast-forwardable Lindbladians imply quantum phase estimation
Zhong-Xia Shang, Naixu Guo, Patrick Rebentrost +3
Quantum phase estimation (QPE) and Lindbladian dynamics are both foundational in quantum information science and central to quantum algorithm design. In this work, we bridge these…
Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Arthur G. Rattew, Po-Wei Huang, Naixu Guo +2
Fault-tolerant Quantum Processing Units (QPUs) promise to deliver exponential speed-ups in select computational tasks, yet their integration into modern deep learning pipelines rem…
DeepMath-Creative: A Benchmark for Evaluating Mathematical Creativity of Large Language Models
Xiaoyang Chen, Xinan Dai, Yu Du +28
To advance the mathematical proficiency of large language models (LLMs), the DeepMath team has launched an open-source initiative aimed at developing an open mathematical LLM and s…
Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
Yuxuan Du, Xinbiao Wang, Naixu Guo +6
This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum compute…
Online Learning of Pure States is as Hard as Mixed States
Maxime Meyer, Soumik Adhikary, Naixu Guo +1
Quantum state tomography, the task of learning an unknown quantum state, is a fundamental problem in quantum information. In standard settings, the complexity of this problem depen…