I am an assistant professor doing theory at Princeton, affiliated with the computer science department and the Princeton Quantum Initiative.
Most recently, I was a Miller postdoctoral fellow at UC Berkeley, hosted by Umesh Vazirani. I received my PhD at the University of Washington, advised by James Lee. Prior to that, I did my undergrad at UT Austin, where I did a thesis advised by Scott Aaronson.
a [CV] (last updated December 2024)
research
I work on quantum computing, often topics that are “quantum + learning”.
Using the jargon, my two main lines of work are in quantum learning theory and dequantized or quantum-inspired algorithms for machine learning.
Some broad motivating questions for me are: How can we evaluate promising applications of quantum computers? How can we design algorithms for understanding nature? and In what settings is engineering a big quantum system tractable?
For more, see my [research] and [teaching] statements.
[google scholar] [talks and teaching]
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The Keyl–Werner algorithm is not optimal for spectrum estimation – Angelos Pelecanos, Jack Spilecki, Ewin Tang, John Wright
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Learning the structure of open quantum systems – Laura Lewis, Ewin Tang, John Wright
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Mixed state tomography reduces to pure state tomography – Angelos Pelecanos, Jack Spilecki, Ewin Tang, John Wright
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A Dobrushin condition for quantum Markov chains: Rapid mixing and conditional mutual information at high temperature – Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang
QIP 2026 -
Conjugate queries can help – Ewin Tang, John Wright, Mark Zhandry [📼]
(QIP 2026 appearing as the merged paper Quantum oracles, weak and strong) -
Are controlled unitaries helpful? – Ewin Tang, John Wright [📼]
(QIP 2026 appearing as the merged paper Quantum oracles, weak and strong) -
Amplitude amplification and estimation require inverses – Ewin Tang, John Wright [📼]
QIP 2026 (appearing as the merged paper Quantum oracles, weak and strong) -
Beating full state tomography for unentangled spectrum estimation – Angelos Pelecanos, Norah Tan, Ewin Tang, John Wright
SODA 2025 -
Learning the closest product state – Ainesh Bakshi, John Bostanci, William Kretschmer, Zeph Landau, Jerry Li, Allen Liu, Ryan O’Donnell, Ewin Tang
QIP 2025 (merged short plenary talk), STOC 2025 -
Structure learning of Hamiltonians from real-time evolution – Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang
FOCS 2024, QIP 2025 [🛝] [📼] -
High-temperature Gibbs states are unentangled and efficiently preparable – Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang
FOCS 2024, QIP 2025 (invited plenary talk) [đź“Ľ]
[Quanta] -
Learning quantum Hamiltonians at any temperature in polynomial time – Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang
QIP 2024 (invited plenary talk & best student paper award), STOC 2024 (invited to SICOMP special issue)
[Quanta] -
Quantum machine learning without any quantum (thesis; [full pdf])
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Do you know what q-means? – Arjan Cornelissen, Joao F. Doriguello, Alessandro Luongo, Ewin Tang
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An improved classical singular value transformation for quantum machine learning – Ainesh Bakshi, Ewin Tang
SODA 2024 [🛝] -
A CS guide to the quantum singular value transformation – Ewin Tang, Kevin Tian
SOSA 2024 [🛝] -
Query-optimal estimation of unitary channels in diamond distance – Jeongwan Haah, Robin Kothari, Ryan O’Donnell, Ewin Tang
FOCS 2023, QIP 2024 [🛝] -
Dequantizing algorithms to understand quantum advantage in machine learning – Ewin Tang
Nature Reviews Physics [brief survey article] -
Optimal learning of quantum Hamiltonians from high-temperature Gibbs states – Jeongwan Haah, Robin Kothari, Ewin Tang
QIP 2022, FOCS 2022, Nature Physics (under the title Learning quantum Hamiltonians from high-temperature Gibbs states and real-time evolutions)
[Nature Physics News & Views] -
An improved quantum-inspired algorithm for linear regression – András Gilyén, Zhao Song, Ewin Tang
Quantum -
Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning – Nai-Hui Chia, András Gilyén, Tongyang Li, Han-Hsuan Lin, Ewin Tang, Chunhao Wang
STOC 2020, QIP 2020, Journal of the ACM -
Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension – András Gilyén, Seth Lloyd, Ewin Tang
ISAAC 2020 -
Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions – Ewin Tang
Physical Review Letters (previously known as Quantum-inspired classical algorithms for principal component analysis and supervised clustering)
[Nature Physics Research Highlight] -
A quantum-inspired classical algorithm for recommendation systems – Ewin Tang
QIP 2020 (invited plenary talk & best student paper award) combined with Quantum-inspired classical algorithms for principal component analysis and supervised clustering, STOC 2019
Co-winner of Best Undergraduate Thesis award, advised by Scott Aaronson
[Shtetl-Optimized] [Quanta] [Nature Editorial] [Communications of the ACM] [UT CS] [Geekwire] [UW CSE] -
Factorizations of k-nonnegative matrices – Sunita Chepuri, Neeraja Kulkarni, Joseph Suk, Ewin Tang
Journal of Combinatorics
contact
email: ent@cs.princeton.edu
Click for info for prospective students.
I am looking for graduate students in the coming 2026–2027 admissions cycle. If you are interested, please apply to either the CS department or the QSE PhD program; I will see your application either way.
I can’t respond to every email, but please know that I do read every email that I receive.