no code implementations • 13 Dec 2023 • Leonard Wossnig, Norbert Furtmann, Andrew Buchanan, Sandeep Kumar, Victor Greiff
Over the past few years, we have observed rapid developments in the field of ML-guided antibody discovery and development (D&D).
no code implementations • 8 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.
no code implementations • 28 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.
no code implementations • 23 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.
no code implementations • 1 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.
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.
no code implementations • 6 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.
no code implementations • 26 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.