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Electronic Colloquium on Computational Complexity

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REPORTS > AUTHORS > SRINIVASAN ARUNACHALAM:
All reports by Author Srinivasan Arunachalam:

TR26-189 | 17th September 2026
Srinivasan Arunachalam, Arkopal Dutt, Sabee Grewal, Aparna Gupte

Marton's conjecture in polynomial time

Gowers, Green, Manners, and Tao (Annals '25) recently resolved Marton's polynomial Freiman–Ruzsa conjecture. We give an algorithmic counterpart to their result: given uniform sampling and membership-oracle access to a set $A \subseteq \mathbb{F}_2^n$ with doubling constant at most $K$, our algorithm outputs a subspace of size at most $|A|$ whose ... more >>>


TR25-130 | 2nd September 2025
Srinivasan Arunachalam, Davi Castro-Silva, Arkopal Dutt, Tom Gur

Algorithmic Polynomial Freiman-Ruzsa Theorems

We prove algorithmic versions of the polynomial Freiman-Ruzsa theorem of Gowers, Green, Manners, and Tao (Annals of Mathematics, 2025) in additive combinatorics. In particular, we give classical and quantum polynomial-time algorithms that, for $A \subseteq \mathbb{F}_2^n$ with doubling constant $K$, learn an explicit description of a subspace $V \subseteq \mathbb{F}_2^n$ ... more >>>


TR23-148 | 3rd October 2023
Srinivasan Arunachalam, Uma Girish, Noam Lifshitz

One Clean Qubit Suffices for Quantum Communication Advantage

We study the one-clean-qubit model of quantum communication where one qubit is in a pure state and all other qubits are maximally mixed. We demonstrate a partial function that has a quantum protocol of cost $O(\log N)$ in this model, however, every interactive randomized protocol has cost $\Omega(\sqrt{N})$, settling a ... more >>>


TR21-178 | 3rd December 2021
Srinivasan Arunachalam, Oded Regev, Penghui Yao

On the Gaussian surface area of spectrahedra

We show that for sufficiently large $n\geq 1$ and $d=C n^{3/4}$ for some universal constant $C>0$, a random spectrahedron with matrices drawn from Gaussian orthogonal ensemble has Gaussian surface area $\Theta(n^{1/8})$ with high probability.

more >>>

TR21-130 | 7th September 2021
Srinivasan Arunachalam, João F. Doriguello

Matrix hypercontractivity, streaming algorithms and LDCs: the large alphabet case

Revisions: 1

Hypercontractive inequalities for real-valued functions over the Boolean cube play an important role in theoretical computer science. In this work, we prove a hypercontractive inequality for matrix-valued functions defined over large alphabets, generalizing the result of Ben-Aroya, Regev, de Wolf (FOCS'08) for the Boolean alphabet. To obtain our result we ... more >>>


TR21-013 | 20th January 2021
Srinivasan Arunachalam, Penghui Yao

Positive spectrahedrons: Geometric properties, Invariance principles and Pseudorandom generators

In a recent work, O'Donnell, Servedio and Tan (STOC 2019) gave explicit pseudorandom generators (PRGs) for arbitrary $m$-facet polytopes in $n$ variables with seed length poly-logarithmic in $m,n$, concluding a sequence of works in the last decade, that was started by Diakonikolas, Gopalan, Jaiswal, Servedio, Viola (SICOMP 2010) and Meka, ... more >>>


TR20-185 | 1st December 2020
Srinivasan Arunachalam, Alex Grilo, Tom Gur, Igor Oliveira, Aarthi Sundaram

Quantum learning algorithms imply circuit lower bounds

Revisions: 1

We establish the first general connection between the design of quantum algorithms and circuit lower bounds. Specifically, let $\mathrm{C}$ be a class of polynomial-size concepts, and suppose that $\mathrm{C}$ can be PAC-learned with membership queries under the uniform distribution with error $1/2 - \gamma$ by a time $T$ quantum algorithm. ... more >>>


TR19-041 | 7th March 2019
Srinivasan Arunachalam, Alex Bredariol Grilo, Aarthi Sundaram

Quantum hardness of learning shallow classical circuits

In this paper we study the quantum learnability of constant-depth classical circuits under the uniform distribution and in the distribution-independent framework of PAC learning. In order to attain our results, we establish connections between quantum learning and quantum-secure cryptosystems. We then achieve the following results.

1) Hardness of learning ... more >>>


TR18-167 | 25th September 2018
Srinivasan Arunachalam, Sourav Chakraborty, Michal Koucky, Nitin Saurabh, Ronald de Wolf

Improved bounds on Fourier entropy and Min-entropy

Revisions: 1

Given a Boolean function $f: \{-1,1\}^n\rightarrow \{-1,1\}$, define the Fourier distribution to be the distribution on subsets of $[n]$, where each $S\subseteq [n]$ is sampled with probability $\widehat{f}(S)^2$. The Fourier Entropy-Influence (FEI) conjecture of Friedgut and Kalai [FK96] seeks to relate two fundamental measures associated with the Fourier distribution: does ... more >>>




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