Yunchao Liu

I am a HQI Postdoctoral Fellow at Harvard University, hosted by Anurag Anshu and Sitan Chen. I got my PhD in Computer Science from UC Berkeley, and I was very fortunate to be advised by Umesh Vazirani. Prior to that, I received my bachelor's degree from Yao Class at Tsinghua University.

My research interests are in quantum information, computation, and complexity theory. Recently, I have been working on establishing theoretical foundations for achieving quantum computational advantage on NISQ and early fault tolerant devices.

Email: yunchaoliu at berkeley dot edu | Google Scholar | Follow @x

Selected Papers

• Rapid mixing for Gibbs states within a logical sector: a dynamical view of self-correcting quantum memories
  Thiago Bergamaschi, Reza Gheissari, Yunchao Liu
  [arXiv]
• On fault tolerant single-shot logical state preparation and robust long-range entanglement
  Thiago Bergamaschi, Yunchao Liu
  16th Innovations in Theoretical Computer Science (ITCS 2025)
  [arXiv]
• Learning quantum states prepared by shallow circuits in polynomial time
  Zeph Landau, Yunchao Liu
  28th Annual Conference on Quantum Information Processing (QIP 2025)
  57th Annual ACM Symposium on Theory of Computing (STOC 2025)
  [arXiv] [talk at Simons] [slides]
• Incompressibility and spectral gaps of random circuits
  Chi-Fang Chen, Jeongwan Haah, Jonas Haferkamp, Yunchao Liu, Tony Metger, Xinyu Tan
  28th Annual Conference on Quantum Information Processing (QIP 2025) plenary talk
  66th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2025)
  [arXiv] [𝕏 post]
• A generalized cycle benchmarking algorithm for characterizing mid-circuit measurements
  Zhihan Zhang, Senrui Chen, Yunchao Liu, Liang Jiang
  PRX Quantum, 2025
  [arXiv]
• Quantum computational advantage with constant-temperature Gibbs sampling
  Thiago Bergamaschi, Chi-Fang Chen, Yunchao Liu
  28th Annual Conference on Quantum Information Processing (QIP 2025)
  65th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2024)
  [arXiv] [talk at IMSI] [slides]
• Efficient approximate unitary designs from random Pauli rotations
  Jeongwan Haah, Yunchao Liu, Xinyu Tan
  28th Annual Conference on Quantum Information Processing (QIP 2025)
  65th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2024)
  [arXiv]
• Learning shallow quantum circuits
  Hsin-Yuan Huang, Yunchao Liu, Michael Broughton, Isaac Kim, Anurag Anshu, Zeph Landau, Jarrod R. McClean
  27th Annual Conference on Quantum Information Processing (QIP 2024) plenary talk
  56th Annual ACM Symposium on Theory of Computing (STOC 2024)
  
  • Invited to the SICOMP special issue for STOC 2024
  [arXiv] [STOC] [talk at Simons] [slides]
• A polynomial-time classical algorithm for noisy random circuit sampling
  Dorit Aharonov, Xun Gao, Zeph Landau, Yunchao Liu, Umesh Vazirani
  26th Annual Conference on Quantum Information Processing (QIP 2023) plenary talk
  55th Annual ACM Symposium on Theory of Computing (STOC 2023)
  
  • Invited to the SICOMP special issue for STOC 2023
  [arXiv] [STOC] [talk at Simons] [slides] [Quanta Magazine]
• The learnability of Pauli noise
  Senrui Chen, Yunchao Liu, Matthew Otten, Alireza Seif, Bill Fefferman, Liang Jiang
  Nature Communications, 2023
  [arXiv] [Nature Communications] [talk at Simons] [slides]
• Distributed quantum inner product estimation
  Anurag Anshu, Zeph Landau, Yunchao Liu
  25th Annual Conference on Quantum Information Processing (QIP 2022)
  54th Annual ACM Symposium on Theory of Computing (STOC 2022)
  [arXiv] [STOC] [talk at STOC] [slides]
• Benchmarking near-term quantum computers via random circuit sampling
  Yunchao Liu, Matthew Otten, Roozbeh Bassirianjahromi, Liang Jiang, Bill Fefferman
  [arXiv] [slides]
• Noise and the frontier of quantum supremacy
  Adam Bouland, Bill Fefferman, Zeph Landau, Yunchao Liu
  24th Annual Conference on Quantum Information Processing (QIP 2021)
  62nd Annual IEEE Symposium on Foundations of Computer Science (FOCS 2021)
  [arXiv] [FOCS] [talk at FOCS] [slides]
• A rigorous and robust quantum speed-up in supervised machine learning
  Yunchao Liu, Srinivasan Arunachalam, Kristan Temme
  Nature Physics, 2021
  [arXiv] [Nature Physics] [slides] [IBM Research Blog] [Quanta Magazine]
• Universal and operational benchmarking of quantum memories
  Xiao Yuan, Yunchao Liu, Qi Zhao, Bartosz Regula, Jayne Thompson, Mile Gu
  23rd Annual Conference on Quantum Information Processing (QIP 2020)
  npj Quantum Information, 2021
  [arXiv] [npj QI]
• Operational resource theory of quantum channels
  Yunchao Liu, Xiao Yuan
  Physical Review Research, 2020
  [arXiv] [PRResearch]
• One-shot coherence distillation: towards completing the picture
  Qi Zhao, Yunchao Liu, Xiao Yuan, Eric Chitambar, Andreas Winter
  IEEE Transactions on Information Theory, 2019
  [arXiv] [IEEE-TIT]
• One-shot coherence dilution
  Qi Zhao, Yunchao Liu, Xiao Yuan, Eric Chitambar, Xiongfeng Ma
  Physical Review Letters, 2018
  [arXiv] [PRL]