Research
We sit at the intersection of computation and physics: using numerical methods to simulate the physical world, and using physics to understand the nature of computation. Our full publication list lives on Google Scholar ↗.
Tensor networks
Contraction-order optimization and generic tensor networks — a unified framework we use to compute solution-space properties of combinatorial optimization problems and to run probabilistic inference on graphical models.
Quantum computation & error correction
Quantum algorithm design and simulation (Yao.jl), tensor-network decoders for quantum error-correcting codes, universal quantum computing with a single arbitrary gate, and algorithms for neutral-atom (Rydberg) hardware.
Computational complexity
The nature of computation from a physics perspective: problem reductions, automated discovery of branching rules with optimal complexity, and embedding hard problems into physical devices.
AI-native scientific computing
Automatic differentiation through numerical methods, and human-AI collaboration workflows for building research software — test-driven vibe coding, agent infrastructure, sustainable automation.
Open-source software
Research here ships as open-source Julia packages. Group organizations: CodingThrust, QuantumBFS, Happy-Binaries.
Extensible, efficient quantum algorithm design framework — one of the fastest quantum circuit simulators.
Einstein-summation engine with hyper-optimized contraction-order search, the backbone of our tensor-network stack.
Solution-space properties of combinatorial optimization problems via generic tensor networks.
Tensor-network decoding and simulation of quantum error-correcting codes.
Reductions between computationally hard problems (spin glass, SAT, ...).
Probabilistic inference on graphical models with tensor networks and differentiable programming.
Selected talks
20 talks since 2023 — expand
- — "100 Years of Matrix Mechanics" International Symposium · Automated Discovery of Branching Rules with Optimal Complexity for the Maximum Independent Set Problem invited talk
- — CompQu online seminar series · Gadget design: towards embedding computational hard problems to physical devices seminar talk
- — Julia for numerical problems in quantum and solid-state physics · Tensor network based quantum simulation with Yao.jl invited talk
- — CCF China Open Source Conference · Large scale tensor network contraction in Julia invited talk
- — Beijing Normal University · Generic tensor networks and its application in combinatorial optimization and probabilistic inference seminar talk
- — JuliaCN 2024 Meetup Organizer
- — CompQMB2024 · Computing solution space properties of combinatorial optimization problems via generic tensor networks invited talk
- — CPS International Young Scientists Forum on Quantum Computing · Fault-tolerant quantum computing with a single arbitrary quantum gate invited talk
- — JuliaCon 2024 · Tensor Network for Quantum Error Correction (by Zhong-Yi Ni) talk [Video]
- — 1st Workshop on Quantum Computation meets Quantum Many-Body Computation · Computing solution space properties via generic tensor networks invited talk
- — 量子信息-青年论坛,合肥国家实验室 · Computer-assisted gadget design and problem reduction of unweighted maximum independent set invited talk
- — Seminars for Quantum Dynamics, 福田量子院 IQA-717 · Computer-assisted gadget design and problem reduction seminar talk [Photo] [Poster]
- — 科学计算与 Julia 技术研讨会 · 量子模拟器 Yao.jl 的回顾与新特性 invited talk
- — Julia User Group Munich · Yao.jl - a high quantum simulator written in pure Julia talk [Video] [Code]
- — 23rd Asian Quantum Information Science Conference Committee member
- — ICIAM 2023 · Computing solution space properties via generic tensor networks poster [Poster]
- — CCMP 2023 · Harnessing Natural Compounds for Universal Quantum Computing invited talk [Slides]
- — 2023多体计算方法讲习班 · Julia for Quantum Many-Body Computation lecture [Video] [Notes]
- — 2023多体计算方法讲习班 · Solving Computational Hard Problems with Tensor Networks lecture [Video] [Notes]
- — 第七届统计物理与复杂系统会议 · Bayesian inference with tensor networks invited talk [Slides]