Search Results for author: Guillaume Verdon

Found 8 papers, 5 papers with code

A semi-agnostic ansatz with variable structure for quantum machine learning

no code implementations11 Mar 2021 M. Bilkis, M. Cerezo, Guillaume Verdon, Patrick J. Coles, Lukasz Cincio

Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization.

Data Compression Quantum Machine Learning

Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm

no code implementations4 Oct 2019 Guillaume Verdon, Jacob Marks, Sasha Nanda, Stefan Leichenauer, Jack Hidary

We introduce a new class of generative quantum-neural-network-based models called Quantum Hamiltonian-Based Models (QHBMs).

Quantum Graph Neural Networks

1 code implementation26 Sep 2019 Guillaume Verdon, Trevor McCourt, Enxhell Luzhnica, Vikash Singh, Stefan Leichenauer, Jack Hidary

We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and are particularly suitable to be executed on distributed quantum systems over a quantum network.

Learning to learn with quantum neural networks via classical neural networks

4 code implementations11 Jul 2019 Guillaume Verdon, Michael Broughton, Jarrod R. McClean, Kevin J. Sung, Ryan Babbush, Zhang Jiang, Hartmut Neven, Masoud Mohseni

Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges.

Meta-Learning Quantum Approximate Optimization

A Quantum Approximate Optimization Algorithm for continuous problems

no code implementations1 Feb 2019 Guillaume Verdon, Juan Miguel Arrazola, Kamil Brádler, Nathan Killoran

We introduce a quantum approximate optimization algorithm (QAOA) for continuous optimization.

Quantum Physics

A Universal Training Algorithm for Quantum Deep Learning

2 code implementations25 Jun 2018 Guillaume Verdon, Jason Pye, Michael Broughton

MoMGrad leverages Baqprop to estimate gradients and thereby perform gradient descent on the parameter landscape; it can be thought of as the quantum-classical analogue of QDD.

Quantum Physics

A quantum algorithm to train neural networks using low-depth circuits

3 code implementations14 Dec 2017 Guillaume Verdon, Michael Broughton, Jacob Biamonte

The question has remained open if near-term gate model quantum computers will offer a quantum advantage for practical applications in the pre-fault tolerance noise regime.

Quantum Physics Disordered Systems and Neural Networks

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