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no code implementations • NeurIPS 2021 • Srinivasan Arunachalam, Yihui Quek, John Smolin

We then show information-theoretic implications between DP learning quantum states in the PAC model, learnability of quantum states in the one-way communication model, online learning of quantum states, quantum stability (which is our conceptual contribution), various combinatorial parameters and give further applications to gentle shadow tomography and noisy quantum state learning.

no code implementations • 20 Jan 2021 • Srinivasan Arunachalam, Penghui Yao

Our main technical contributions are the following: We first prove an invariance principle for positive spectrahedra via the well-known Lindeberg method.

Computational Complexity Combinatorics

no code implementations • 3 Dec 2020 • Srinivasan Arunachalam, Alex B. Grilo, Tom Gur, Igor C. Oliveira, Aarthi Sundaram

This result is optimal in both $\gamma$ and $T$, since it is not hard to learn any class $\mathfrak{C}$ of functions in (classical) time $T = 2^n$ (with no error), or in quantum time $T = \mathsf{poly}(n)$ with error at most $1/2 - \Omega(2^{-n/2})$ via Fourier sampling.

no code implementations • 5 Oct 2020 • 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.

1 code implementation • 15 Apr 2020 • Anurag Anshu, Srinivasan Arunachalam, Tomotaka Kuwahara, Mehdi Soleimanifar

In this work, we give the first sample-efficient algorithm for the quantum Hamiltonian learning problem.

no code implementations • 19 Feb 2020 • Srinivasan Arunachalam, Alex B. Grilo, Henry Yuen

Additionally, we show that in the private PAC learning setting, the classical and quantum sample complexities are equal, up to constant factors.

no code implementations • ICML 2020 • Srinivasan Arunachalam, Reevu Maity

To this end, suppose we have a $\gamma$-weak quantum learner for a Boolean concept class $C$ that takes time $Q(C)$, we introduce a quantum boosting algorithm whose complexity scales as $\sqrt{VC(C)}\cdot poly(Q(C), 1/\gamma);$ thereby achieving a quadratic quantum improvement over classical AdaBoost in terms of $VC(C)$.

no code implementations • 7 Mar 2019 • Srinivasan Arunachalam, Alex B. Grilo, Aarthi Sundaram

The main technique in this result is to establish a connection between the quantum security of public-key cryptosystems and the learnability of a concept class that consists of decryption functions of the cryptosystem.

no code implementations • 30 Sep 2018 • Srinivasan Arunachalam, Sourav Chakraborty, Troy Lee, Manaswi Paraashar, Ronald de Wolf

We present two new results about exact learning by quantum computers.

no code implementations • 1 Nov 2017 • András Gilyén, Srinivasan Arunachalam, Nathan Wiebe

We also show that in a continuous phase-query model, our gradient computation algorithm has optimal query complexity up to poly-logarithmic factors, for a particular class of smooth functions.

Quantum Physics Computational Complexity

no code implementations • 24 Jan 2017 • Srinivasan Arunachalam, Ronald de Wolf

This paper surveys quantum learning theory: the theoretical aspects of machine learning using quantum computers.

no code implementations • 4 Jul 2016 • Srinivasan Arunachalam, Ronald de Wolf

This shows classical and quantum sample complexity are equal up to constant factors.

no code implementations • 11 Feb 2015 • Srinivasan Arunachalam, Vlad Gheorghiu, Tomas Jochym-O'Connor, Michele Mosca, Priyaa Varshinee Srinivasan

We introduce a circuit model for the quantum bucket brigade architecture and argue that quantum error correction for the circuit causes the quantum bucket brigade architecture to lose its primary advantage of a small number of "active" gates, since all components have to be actively error corrected.

Quantum Physics

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