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20162023
most citedQuantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group

211 citations · 241 across the 9 of their papers we have counts for

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Showing 2020Show all

8 papers · 1 filter

quant-ph2020

Quantum learning algorithms imply circuit lower bounds

Srinivasan Arunachalam, Alex B. Grilo, Tom Gur +2

We establish the first general connection between the design of quantum algorithms and circuit lower bounds. Specifically, let be a class of polynomial-size concepts…

quant-ph2020

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…

quant-ph2020

Simpler (classical) and faster (quantum) algorithms for Gibbs partition functions

Srinivasan Arunachalam, Vojtech Havlicek, Giacomo Nannicini +2

We present classical and quantum algorithms for approximating partition functions of classical Hamiltonians at a given temperature. Our work has two main contributions: first, we m…

cs.CC2020

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…

quant-ph2020

Sample-efficient learning of quantum many-body systems

Anurag Anshu, Srinivasan Arunachalam, Tomotaka Kuwahara +1

We study the problem of learning the Hamiltonian of a quantum many-body system given samples from its Gibbs (thermal) state. The classical analog of this problem, known as learning…

quant-ph2020

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…