Ewin Tang

Orcid: 0000-0002-7451-9687

Affiliations:
  • University of Washington, WA, USA


According to our database1, Ewin Tang authored at least 15 papers between 2018 and 2024.

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Bibliography

2024
Structure learning of Hamiltonians from real-time evolution.
CoRR, 2024

High-Temperature Gibbs States are Unentangled and Efficiently Preparable.
CoRR, 2024

Learning Quantum Hamiltonians at Any Temperature in Polynomial Time.
Proceedings of the 56th Annual ACM Symposium on Theory of Computing, 2024

A CS guide to the quantum singular value transformation.
Proceedings of the 2024 Symposium on Simplicity in Algorithms, 2024

An Improved Classical Singular Value Transformation for Quantum Machine Learning.
Proceedings of the 2024 ACM-SIAM Symposium on Discrete Algorithms, 2024

2023
Do you know what q-means?
CoRR, 2023

Query-optimal estimation of unitary channels in diamond distance.
Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

2022
An improved quantum-inspired algorithm for linear regression.
Quantum, 2022

Sampling-based Sublinear Low-rank Matrix Arithmetic Framework for Dequantizing Quantum Machine Learning.
J. ACM, 2022

Optimal learning of quantum Hamiltonians from high-temperature Gibbs states.
Proceedings of the 63rd IEEE Annual Symposium on Foundations of Computer Science, 2022

Demo: Visualizing USSD and IVR Usage Data with Icicle Charts.
Proceedings of the COMPASS '22: ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, Seattle, WA, USA, 29 June 2022, 2022

2020
Quantum-Inspired Algorithms for Solving Low-Rank Linear Equation Systems with Logarithmic Dependence on the Dimension.
Proceedings of the 31st International Symposium on Algorithms and Computation, 2020

2018
A quantum-inspired classical algorithm for recommendation systems.
Electron. Colloquium Comput. Complex., 2018

Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension.
CoRR, 2018

Quantum-inspired classical algorithms for principal component analysis and supervised clustering.
CoRR, 2018


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