| 2026 | SODA | Beating full state tomography for unentangled spectrum estimation. | Angelos Pelecanos, Xinyu Tan, Ewin Tang, John Wright |
| 2026 | STOC | A Dobrushin Condition for Quantum Markov Chains: Rapid Mixing and Conditional Mutual Information at High Temperature. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2025 | STOC | Learning the Closest Product State. | Ainesh Bakshi, John Bostanci, William Kretschmer, Zeph Landau, Jerry Li, Allen Liu, Ryan O'Donnell, Ewin Tang |
| 2024 | FOCS | High-Temperature Gibbs States are Unentangled and Efficiently Preparable. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2024 | FOCS | Structure Learning of Hamiltonians from Real-Time Evolution. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2024 | SODA | An Improved Classical Singular Value Transformation for Quantum Machine Learning. | Ainesh Bakshi, Ewin Tang |
| 2024 | STOC | Learning Quantum Hamiltonians at Any Temperature in Polynomial Time. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2023 | FOCS | Query-optimal estimation of unitary channels in diamond distance. | Jeongwan Haah, Robin Kothari, Ryan O'Donnell, Ewin Tang |
| 2022 | FOCS | Optimal learning of quantum Hamiltonians from high-temperature Gibbs states. | Jeongwan Haah, Robin Kothari, Ewin Tang |
| 2020 | ISAAC | Quantum-Inspired Algorithms for Solving Low-Rank Linear Equation Systems with Logarithmic Dependence on the Dimension. | Nai-Hui Chia, Andrs Gilyn, Han-Hsuan Lin, Seth Lloyd, Ewin Tang, Chunhao Wang |
| 2020 | STOC | Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning. | Nai-Hui Chia, Andrs Gilyn, Tongyang Li, Han-Hsuan Lin, Ewin Tang, Chunhao Wang |
| 2019 | STOC | A quantum-inspired classical algorithm for recommendation systems. | Ewin Tang |