8 citations · 24 across the 15 of their papers we have counts for
16 papers
Evaluating Verified Autonomy in Quantum Engineering
Naixu Guo, Changhao Li, Siyu Cheng +6
Reliable quantum engineering is essential for turning quantum phenomena into practical technologies. As quantum platforms grow in scale and complexity, their characterization and o…
Near-Optimal Learning of Local Lindbladians
Itai Arad, Zhili Chen, Naixu Guo +2
We study the problem of learning local Lindbladians from black-box access to the physical evolution, where the goal is to estimate all Hamiltonian and dissipative coefficients. For…
Quantum enhanced rare event discovery and sampling
Naixu Guo, Po-Wei Huang, Qisheng Wang +4
Financial crashes, cascading failures in infrastructure, and critical errors in AI systems are frequently triggered by events that occur with extremely small probability. Efficient…
COMPOSITE-Stem
Kyle Waters, Lucas Nuzzi, Tadhg Looram +20
AI agents hold growing promise for accelerating scientific discovery; yet, a lack of frontier evaluations hinders adoption into real workflows. Expert-written benchmarks have prove…
AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios
Kaiyuan Chen, Qimin Wu, Taiyu Hou +42
The capacity of AI agents to effectively handle tasks of increasing duration and complexity continues to grow, demonstrating exceptional performance in coding, deep research, and c…
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…
Design nearly optimal quantum algorithm for linear differential equations via Lindbladians
Zhong-Xia Shang, Naixu Guo, Dong An +1
Solving linear ordinary differential equations (ODE) is one of the most promising applications for quantum computers to demonstrate exponential advantages. The challenge of designi…
Quantum Transformer: Accelerating model inference via quantum linear algebra
Naixu Guo, Zhan Yu, Matthew Choi +5
Powerful generative artificial intelligence from large language models (LLMs) harnesses extensive computational resources for inference. In this work, we investigate the transforme…
Provable learning of quantum states with graphical models
Liming Zhao, Naixu Guo, Ming-Xing Luo +1
The complete learning of an -qubit quantum state requires samples exponentially in . Several works consider subclasses of quantum states that can be learned in polynomial sam…
Estimating properties of a quantum state by importance-sampled operator shadows
Naixu Guo, Feng Pan, Patrick Rebentrost
Measuring properties of quantum systems is a fundamental problem in quantum mechanics. We provide a simple method for estimating the expectation value of observables with an unknow…
Quantum Alphatron: quantum advantage for learning with kernels and noise
Siyi Yang, Naixu Guo, Miklos Santha +1
At the interface of machine learning and quantum computing, an important question is what distributions can be learned provably with optimal sample complexities and with quantum-ac…
Nonlinear transformation of complex amplitudes via quantum singular value transformation
Naixu Guo, Kosuke Mitarai, Keisuke Fujii
Due to the linearity of quantum operations, it is not straightforward to implement nonlinear transformations on a quantum computer, making some practical tasks like a neural networ…