Search Results for author: Leonard Wossnig

Found 7 papers, 0 papers with code

Quantum Machine Learning For Classical Data

no code implementations8 May 2021 Leonard Wossnig

Looking for areas which might bear larger advantages for QML algorithms, we finally propose a novel algorithm for Quantum Boltzmann machines, and argue that quantum algorithms for quantum data are one of the most promising applications for QML with potentially exponential advantage over classical approaches.

Learning Theory

Statistical Limits of Supervised Quantum Learning

no code implementations28 Jan 2020 Carlo Ciliberto, Andrea Rocchetto, Alessandro Rudi, Leonard Wossnig

Within the framework of statistical learning theory it is possible to bound the minimum number of samples required by a learner to reach a target accuracy.

Learning Theory

Generative training of quantum Boltzmann machines with hidden units

no code implementations23 May 2019 Nathan Wiebe, Leonard Wossnig

In this article we provide a method for fully quantum generative training of quantum Boltzmann machines with both visible and hidden units while using quantum relative entropy as an objective.

Adversarial quantum circuit learning for pure state approximation

no code implementations1 Jun 2018 Marcello Benedetti, Edward Grant, Leonard Wossnig, Simone Severini

Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data.

Quantum State Tomography

Universal discriminative quantum neural networks

no code implementations ICLR 2019 Hongxiang Chen, Leonard Wossnig, Simone Severini, Hartmut Neven, Masoud Mohseni

This circuit learns to simulates the unknown structure of a generalized quantum measurement, or Positive-Operator-Value-Measure (POVM), that is required to optimally distinguish possible distributions of quantum inputs.

Stochastic Optimization

Approximating Hamiltonian dynamics with the Nyström method

no code implementations6 Apr 2018 Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil, Simone Severini

Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers.

Quantum machine learning: a classical perspective

no code implementations26 Jul 2017 Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, Leonard Wossnig

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.

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