7 citations · 7 across the 2 of their papers we have counts for
9 papers
Private learning implies quantum stability
Srinivasan Arunachalam, Yihui Quek, John Smolin
Learning an unknown -qubit quantum state is a fundamental challenge in quantum computing. Information-theoretically, it is known that tomography requires exponential in …
Positive spectrahedra: Invariance principles and Pseudorandom generators
Srinivasan Arunachalam, Penghui Yao
In a recent work, O'Donnell, Servedio and Tan (STOC 2019) gave explicit pseudorandom generators (PRGs) for arbitrary -facet polytopes in variables with seed length poly-loga…
A rigorous and robust quantum speed-up in supervised machine learning
Yunchao Liu, Srinivasan Arunachalam, Kristan Temme
Over the past few years several quantum machine learning algorithms were proposed that promise quantum speed-ups over their classical counterparts. Most of these learning algorithm…
Communication memento: Memoryless communication complexity
Srinivasan Arunachalam, Supartha Podder
We study the communication complexity of computing functions in the memoryless communication model. Here, Alice is given $x\in \{0…
Quantum Coupon Collector
Srinivasan Arunachalam, Aleksandrs Belovs, Andrew M. Childs +3
We study how efficiently a -element set can be learned from a uniform superposition of its elements. One can think of $|S\rangle=\sum_{i\in S}|i\rang…
Quantum statistical query learning
Srinivasan Arunachalam, Alex B. Grilo, Henry Yuen
We propose a learning model called the quantum statistical learning QSQ model, which extends the SQ learning model introduced by Kearns to the quantum setting. Our model can be als…