Yihui Quek

Hello! I am a fifth-year PhD student at Stanford University. My research interests entangle Physics, information theory (quantum/classical) and algorithms.

My advisor is Prof. Tsachy Weissman and my undergraduate advisor at MIT was Prof. Peter W. Shor. I was a devoted quantum Shannon theorist for the first half of my PhD; after wandering into some excellent classes at Stanford, I also became interested in algorithms and theoretical computer science!

In June 2016, I graduated with a B.S. in Physics and Mathematics (Phi Beta Kappa) from MIT. I wrote my senior thesis on quantum and super-quantum enhancements to capacities of interference channels, supervised by Prof. Peter W. Shor (because of this my Erdös number is 3!).

I am one of the two inaugural recipients of the Stanford Q-FARM Fellowship. I'm also supported by an National University of Singapore (NUS) Overseas Graduate Scholarship. From 2017-2020, I was supported by a Stanford Graduate Fellowship.

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- (Oct 20) The workshop Beyond i.i.d. in Information Theory (which I am an organizer/PC member for) was successfully held at Stanford University! I also gave a lightning talk about our recent work on bipartite channel capacities, which gives the tightest known general bound on the classical feedback-assisted quantum channel capacity.

- (Jul-Oct 20) I spoke about our algorithm for implementing the Petz map, pretty-good measurements and polar decomposition at the MIT QIP group meeting, the workshop `Quantum Week of Fun' (organized by Cambridge Quantum Computing) and the Perimeter Institute Quantum Seminar. Here are my slides.

- (Apr/May 20) I gave an invited talk at the Algorithms and Complexity seminar of the IRIF (a research laboratory of CNRS and Université de Paris); and also the UTS QSI Seminar on Robust Quantum Minimum Finding. Here are my slides.

- (Feb 20) I was one of 600 young scientists (globally, across all disciplines) invited to participate in the 2020 Lindau Nobel Laureate Meeting (Interdisciplinary) in Lindau, Germany.

- (Feb 20) My collaborator Stanislav Fort gave an invited talk at the Stanford-SLAC Quantum Initiative (Q-FARM) on our work, entitled Learning Adaptive Quantum State Tomography with Neural Networks and Differentiable Programming. You can find Stan's awesome slides on our project here.

- (Oct 19) I was awarded an NUS Development Grant -- a SGD 10000 grant from the National University of Singapore to cover research and travel expenses of overseas Singaporean scientists.

- (Oct 19) I gave a talk on Adaptive Quantum State Tomography with Neural Networks at the Quantum Technologies in Machine Learning Workshop 2019 at KAIST, Daejeon, South Korea.

- (Sep 19) I am one of two inaugural recipients of the Stanford Q-FARM Fellowship!

  • 2017/1 - Present: Ph.D candidate, Stanford University
  • 2012/8 - 2016/6: B.Sc, Massachusetts Institute of Technology (GPA: 4.9/5.0)
  • 2006/1 - 2011/1: NUS High School of Mathematics and Science, Singapore (Rank: 1/209)

Reviewer for Physical Review Letters, Physical Review A, ISIT (International Symposium on Information Theory), Quantum, SODA (Symposium on Discrete Algorithms), IEEE Quantum Engineering.

I am also one of the organizers for the Beyond I.I.D. in Information Theory workshop to be held at Stanford in 2020.

Quantum Shannon theory

Bounding the forward classical channel capacity of bipartite channels
Dawei Ding, Sumeet Khatri, Yihui Quek, Peter W. Shor, Xin Wang, Mark M Wilde
[arXiv], [short talk], [long talk (by Mark)]

We derive an SDP upper-bound on the bipartite channel's classical capacity. As a result, we also obtain the tightest-known upper-bound on classical-feedback-assisted quantum channel capacity, in a sequel to our first paper on the topic.

Lightning talk at the Beyond i.i.d in Information Theory workshop

Entropy Bound for the Classical Capacity of a Quantum Channel aided by Classical Feedback
Dawei Ding, Yihui Quek, Peter W. Shor, Mark M Wilde
2019 IEEE International Symposium on Information Theory (ISIT), Paris, France, 2019, pp. 250-254
[arXiv], [IEEE]

First-ever general bound on classical feedback-aided capacity over a quantum channel, in terms of the maximum output entropy of that channel.

Contributed talk at the International Symposium for Information Theory

Quantum and Super-Quantum Enhancements to Two-sender, Two-receiver Channels
Yihui Quek and Peter W. Shor
Physical Review A, Vol.95, No.5, May 1, 2017
[arxiv][Physical Review A]

Poster at Young Quantum Information Scientists Symposium in Barcelona, 2016

Quantum Algorithms
Quantum algorithm for Petz recovery channels and pretty good measurements
A Gilyén, S Lloyd, I Marvian, Y Quek, MM Wilde
[arXiv], [talk], [slides], [Limerick]

We use the recently-developed Quantum Singular Value Transform technique to implement the ubiquitous theoretical tools of Petz recovery channels and pretty good measurements.

Talk at the MIT QIS group meeting; invited talk at the Perimeter Institute Quantum Seminar; contributed talk at the `Quantum Week of Fun' workshop

Robust Quantum Minimum Finding with an Application to Hypothesis Selection
Yihui Quek, Clément Canonne, Patrick Rebentrost
[arXiv], [slides], [Limerick]

We show that the Quantum Minimum-Finding algorithm of Durr-Hoyer can be robust even in the presence of a noisy or imprecise comparator. We also show an application to hypothesis selection that runs in time sublinear in the number of hypotheses.

Invited talk at the Algorithms and Complexity Seminar of the IRIF at CNRS/the University of Paris; talk at online seminar at the University of Technology Sydney's QSI.

Quantum Information Theory

Adaptive Quantum State Tomography with Neural Networks
Yihui Quek , Stanislav Fort, Hui Khoon Ng
[arXiv], [Slides(by Stanislav)] here

We design a recurrent neural network architecture for adaptive quantum state tomography, achieving an orders-of-magnitude speedup over an existing Bayesian algorithm for realistic numbers of measurements while retaining the same reconstruction accuracy.

Invited talk at Stanford Q-FARM seminar (Feb 2020); contributed talks at 3rd Quantum Techniques in Machine Learning 2019 (QTML) in Korea and McGill Physics-AI conference in Montreal; accepted at the 4th Seefeld Workshop on Quantum Information, 22nd Annual Conference on Quantum Information Processing (QIP 2019) and the Machine Learning and the Physical Sciences Workshop at NeurIPS 2019 as a poster.

Signal processing, Biophysics, Linguistics
Minimum Power to Maintain a Nonequilibrium Distribution of a Markov Chain
Dmitri Pavlichin, Yihui Quek, Tsachy Weissman

Inspired by a question of Feynman, we propose KL-divergence between Markov chains as a notion of energy cost for maintaining a nonequilibrium distribution in biological systems.

Body size-dependent energy storage causes Kleiber's law scaling of the metabolic rate in planarians
Albert Thommen, Steffen Werner, Olga Frank, Jenny Philipp, Oskar Knittelfelder, Yihui Quek, Karim Fahmy, Andrej Shevchenko, Benjamin M. Friedrich, Frank Jülicher, Jochen C. Rink
eLife, 8 Art. No. e38187 (2019)
[bioRxiv] [e-Life]

Contributed talk at 1st Crick-Beddington Developmental Biology Symposium, 2019

Severing focus form and meaning in Standard and Colloquial Singapore English
Yihui Quek and Aron Hirsch
Proceedings of the 47th meeting of the North-east Linguistics Society (NELS 47), 2016

Poster at NELS47, 2016

Generalized Robust Shrinkage Estimator and its application to STAP detection problem
Frédéric Pascal, Yacine Chitour and Yihui Quek
IEEE Transactions on Signal Processing, Vol. 62, No. 21, Nov 1, 2014

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