Search Results for author: Srinivasan Arunachalam

Found 13 papers, 1 papers with code

Private learning implies quantum stability

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.

Learning Theory online learning +1

Positive spectrahedra: Invariance principles and Pseudorandom generators

no code implementations20 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

Quantum learning algorithms imply circuit lower bounds

no code implementations3 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.

Learning Theory

A rigorous and robust quantum speed-up in supervised machine learning

no code implementations5 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.

BIG-bench Machine Learning Quantum Machine Learning

Sample-efficient learning of quantum many-body systems

1 code implementation15 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.

BIG-bench Machine Learning

Quantum statistical query learning

no code implementations19 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.

PAC learning

Quantum Boosting

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 $VC(C)$.

Quantum hardness of learning shallow classical circuits

no code implementations7 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.

PAC learning

Optimizing quantum optimization algorithms via faster quantum gradient computation

no code implementations1 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

A Survey of Quantum Learning Theory

no code implementations24 Jan 2017 Srinivasan Arunachalam, Ronald de Wolf

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

BIG-bench Machine Learning Learning Theory

Optimal Quantum Sample Complexity of Learning Algorithms

no code implementations4 Jul 2016 Srinivasan Arunachalam, Ronald de Wolf

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

Learning Theory

On the robustness of bucket brigade quantum RAM

no code implementations11 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

Cannot find the paper you are looking for? You can Submit a new open access paper.