Talks
Talks §
Jointly prepared and presented with Tharrmashastha SAPV.
Invited talk (Young Scientists Session) at the Symposium on Quantum Computing and Artificial Intelligence.
Based on our paper.
Given at CMI as part of the 2024 Quantum Computing Semester.
On the Ajtai 1996 paper. See also Micciancio-Regev and Alwen-Peikert.
See the original paper and blog posts by Boaz Barak, Scott Aaronson, and Lance Fortnow.
HHL notes are part of the "Teach Me Quantum" course for the 2019–20 IBMQ awards (second place prize). See also: HHL '09, Ambainis '10, WZP '17, CKS '17.
Talk for Evariste — IIITD's Theory and Math club. On separation results between SQCs and classical circuits. Papers: Bravyi-Gosset-Koenig '18, Watts et al. '19, Le Gall '19, Bravyi et al. '19.
§ See CV for full list.
Resources
Courses and Books (TCS)
Complexity Theory
- Circuit Complexity by William Hoza.
- Advanced Complexity Theory by Hamed Hatami.
- Computational Complexity by Manoj M. Prabhakaran.
- Communication Complexity by Mark Bun.
Boolean Functions
- Analysis of Boolean Functions by Ryan O'Donnell.
- Analysis of Boolean Functions by Ramprasad Saptarishi.
- Playlist on Analysis of Boolean Functions at the Simons Institute.
Others
- Randomized Algorithms by James Aspnes.
- High Dimensional Expanders by Irit Dinur.
- High Dimensional Expanders by Gil Kalai and Alex Lubotzky.
- Computational Social Choice by Rohit Vaish.
- Analytical Toolkit in Computer Science by Hemanta K. Maji.
- A Theorist's Toolkit by Ryan O'Donnell.
- Data Stream Algorithms by Amit Chakraborti.
- Theory of Metric Embeddings by Harald Räcke.
- Lecture notes on metric embeddings by Jiří Matoušek.
- Algorithms by Jeff Erickson.
- Introduction to Theoretical Computer Science by Boaz Barak.
- Lecture Notes by Tim Roughgarden.
- Probabilistic Method in Combinatorics by Yufei Zhao.
Free Courses and Books (Quantum)
- Introduction to Quantum Computing by Sevag Gharibian.
- Introduction to Quantum Computing by Nathan Wiebe.
- Introduction to Quantum Information Theory by Mark M. Wilde.
- Quantum Complexity Theory by Scott Aaronson.
- Quantum Complexity Theory by Sevag Gharibian.
- Quantum Computing lecture notes by Ronald de Wolf.
- Quantum algorithms: A survey of applications and end-to-end complexities.
- Quantum Walks and Search Algorithms by Renato Portugal.
Free Courses and Books (Learning Theory and Optimization)
- Overview of Statistical Learning Theory: Part 1 and Part 2 by Nati Srebro.
- Sample Complexity: Part 1 and Part 2 by Eli Upfal.
- Machine Learning by Ronald Rivest and Mona Singh.
- Machine Learning Theory by Akshay Krishnamurthy.
- Foundations of Modern Machine Learning by Nika Haghtalab.
- Learning Theory by Ambuj Tewari.
- Machine Learning Theory by Maria Florina Balcan.
- Understanding ML by Shai Shalev-Shwartz and Shai Ben-David.
- Convex Optimization by Sébastien Bubeck.
- Optimization Algorithms by Constantine Caramanis.
- Mathematics of Machine Learning 2019 Summer School.
- Distribution Testing by Clément Canonne.
- Applied Information Theory by Ziv Goldfeld.
Free Courses and Books (Maths)
- Introduction to Measure Theory by Claudio Landim.
- Concentration inequalities by Boucheron, Lugosi, and Massart.
- Concentration of Measure for Randomised Algorithms by Dubhashi and Panconesi.
- Introduction to Group Theory by Richard E Borcherds.
- Introduction to Ring Theory by Elliot Nicholson.
- Introduction to Field Theory by Elliot Nicholson.
- Martingale Theory by Artem Sapozhnikov.
Expository Resources (TCS)
- Computational Complexity (Blog) by Lance Fortnow and Bill Gasarch.
- Windows on Theory (Blog) by Boaz Barak.
- Combinatorics and more (Blog) by Gil Kalai.
- Thoughts by Manu (Blog) by Emanuel Viola.
- What's New? (Blog) by Terrence Tao.
- An introductory survey on expanders by Avi Wigderson. [Video]
- Taxonomy of Square Matrices.
- Metric Embeddings (FOCS22 workshop).
- Five Proofs of Chernoff's Bound by Wolfgang Mulzer.
- Edmond's Blossom Algorithm by James S. Plank.
- Tossing a biased coin by Michael Mitzenmacher.
- Determining the direction of a coin's bias by William Hoza.
- Part 1 and Part 2 on Multivariate Gaussians by Chuong B. Do.
- Understanding Ladner's Theorem by Sanjoy Das.
- Reservoir Sampling: Florian Hartmann, Stephen N. Pallone.
- Morris' algorithm by Gregory Gundersen.
- The Unique Games Conjecture by Scott Aaronson.
- Hastad's switching lemma by Victor Lecomte.
- Plancherel's trick by Victor Lecomte.
Expository Resources (Quantum)
- Shtetl Optimized (Blog) by Scott Aaronson.
- The General Adversary Bound: A Survey by Lily Li and Morgan Shirley.
- HSP — Review and Open Problems by Chris Lomont.
- A Survey on HSP by Frédéric Wang.
- Post Quantum Cryptography by Chris Peikert.
- Quantum algorithms: A survey of applications and end-to-end complexities by Dalzell et al.
- Quantum linear systems algorithms: a primer by Dervovic et al.
- Density Matrix and Mixed States (playlist) by Diego Emilio.
- Maximally mixed vs Maximally Entangled on StackExchange.
- QAOA literature survey by Kunal Marwaha.
- A list of Open and Solved quantum problems by IQOQI Vienna.
Expository Resources (Learning Theory)
- Francis Bach's blog.
- Understanding Optimal Transport by Alex Williams.
- Website on Algorithms With Predictions.
- Diffusion Models: Calvin Luo, Lilian Weng, CVPR 2022 Tutorial.
- Generative Flows: Lilian Weng, Yoshua Bengio.
- Learning Theory Notes by Gene Li.
- Dijkstra's in Disguise by Eric Jang.
Expository Resources (Others)
- Probability Cheatsheet by William Chen and Joe Blitzstein.
- Inequalities Cheatsheet by László Kozma.
- Notes and visualizations on Linear Algebra by Kenji Hiranabe.
- The We(a)ekly Quiz by Clement Cannone.
- Expository Papers on Algebra and Number Theory by Keith Conrad.
Curated Articles (On Research)
- The "Three Things" Exercise for getting things out of talks by Ravi Vakil.
- How to win a Best Paper award by Nicholas Carlini.
- Advice on giving talks by Anupam Gupta.
- The Value of Science by Richard Feynman.
- The Limits of Quantum Computers by Scott Aaronson.
- Advice to PhD students by Oded Goldreich.
- 3 qualities of successful PhD students by Matt Might.
- Writing a good introduction by Jim Kurose.
- How to write a ML paper by Jakob Foerster.
- Writing Technical Articles by Henning Schulzrinne.
- The importance of stupidity in scientific research by Martin A. Schwartz.
- Presenting a Technical Talk by Nick Feamster.
- Top 10 ways to Lose your Audience by William Cook.
- Graduate Student Guide by Lance Fortnow and Bill Gasarch.
- Three Sins of Authors in Computer Science and Math by Jonathan Shewchuk.
Curated Articles (Others)
- Andrei Nikolaevich Kolmogorov by K R Parthasarathy.
- Meeting a Gorilla by Douglas Adams and Mark Carwardine.