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Ewin Tang | |
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| Born | 2000 (age 24–25) |
| Alma mater | University of Texas at Austin (BS) University of Washington (PhD) |
| Awards | Maryam Mirzakhani New Frontiers Prize (2025) |
| Scientific career | |
| Fields | Theoretical computer science Quantum computing |
| Doctoral advisor | James Lee |
| Other academic advisors | Scott Aaronson |
| Website | https://ewintang.com/ |
Ewin Tang (born 2000) is a computer scientist at the University of California, Berkeley. She was named as one of 2019 Science Forbes 30 Under 30 [1] for her work developing classical algorithms which matched the performance of the fastest known quantum algorithms, done as an undergraduate under the supervision of Scott Aaronson.
Tang skipped the fourth, fifth, and sixth grades to take courses at a local high school and at the University of Texas at Arlington at the age of 10, [2] [3] and then enrolled at the University of Texas at Austin at the age of 14. [4] Tang first began research working with her mother, Wen-Jing Hu, in the nanotechnology laboratory of her father, Liping Tang, [2] on in vivo imaging for biomedical research such as optical probes to view polarized macrophages during foreign body reactions, [pub 1] bacterial infection, [pub 2] fibrin deposition, [pub 3] and real-time detection of neutrophil responses. [pub 4]
In 2014 Tang was awarded an Davidson Fellow Honorable Mention for her work on an optical imaging probe for real-time detection of infection. [5] In 2017 she took a class on quantum information taught by Scott Aaronson, who became her undergraduate thesis adviser. Aaronson recognised Tang as an "unusually talented student" and presented her with a range of research projects to choose from; among them was the recommendation problem. [4]
Tang's first work in quantum computing was her 2018 undergraduate thesis titled A quantum-inspired classical algorithm for recommendation systems, [pub 5] supervised by Scott Aaronson as a part of her undergraduate degree in computer science and in pure mathematics from UT Austin. The thesis gives a new algorithm that solves a matrix completion problem, motivated by applications to recommendation systems.
Before Tang's thesis, the best known classical algorithms for matrix completion were exponentially slower, under some assumptions, than the best quantum algorithm for the same problem. Inspired by the quantum algorithms, which were based on the HHL algorithm, she found [pub 5] [pub 6] [pub 7] classical algorithms solving these problem in a similar time as the quantum algorithms, under similar assumptions, thus "dequantizing" them and exponentially improving over the best known classical algorithms.
The best known quantum algorithm for matrix completion had been introduced in 2016 by Iordanis Kerenidis and Anupam Prakash, which runs exponentially faster, in polylogarithmic time, and uses the HHL algorithm as a subroutine. [6] Tang's algorithm also runs in polylogarithmic time, though Tang's algorithm uses a classical analog of the quantum sampling techniques. Prior to Tang's results, it was widely assumed that no fast classical algorithm existed; Kerenidis and Prakash did not attempt to study the classical solution, and the task assigned to Tang by Aaronson was to prove its nonexistence. Before the results were made public, Tang presented a preliminary version of the algorithm at a quantum computing workshop in June 2018 at the University of California, where the audience included Aaronson, Kerenidis, and Prakash. [7] After four hours of questioning, the consensus was that Tang's classical algorithm seemed correct. Tang published her results in STOC in June 2019, [pub 5] and in Physical Review Letters in August 2021. [pub 6]
In 2018 Tang was named as a University of Texas at Austin Dean's Honored Graduate in computer science, having maintained a 4.0 grade-point average. [8]
In 2023 Tang completed her Ph.D. in theoretical computer science at the University of Washington under the supervision of James Lee, [9] where she continued her undergraduate work on quantum-inspired classical algorithms for other problems, such as principal component analysis [pub 6] and low-rank stochastic regression. [pub 7]
There was wide media coverage in response to Tang's work on the recommendation problem, which was perceived as eliminating one of the best examples of quantum speedup. [4] [10] [11] [12] Some researchers defended quantum computing approaches, such as Robert Young, director of the Quantum Technology Centre at Lancaster University, who said "If we hadn't invested in quantum computing, the quantum algorithm that inspired [Ms] Tang wouldn't have existed." [11] Tang herself noted the divisive nature of comparing classical to quantum algorithms, and the trepidation of proving her algorithm to her adviser, "I started believing there is a fast classical algorithm, but I couldn’t really prove it to myself because Scott [Aaronson] seemed to think there wasn’t one, and he was the authority." [4]
In 2019, at the age of 18, Tang was named as one of Forbes 30 Under 30 for developing a computing method "allowing regular computers to solve a particular problem as quickly as a quantum computer." [13]
In 2025, she received a Maryam Mirzakhani New Frontiers Prize for "developing classical analogs of quantum algorithms for machine learning and linear algebra, and for advances in quantum machine learning on quantum data." [14] This prize is given to women mathematicians who completed their PhD's within the past two years. Each year, up to three of the prizes can be awarded.
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