Search Results for author: Nathan Killoran

Found 14 papers, 9 papers with code

Estimating the gradient and higher-order derivatives on quantum hardware

1 code implementation14 Aug 2020 Andrea Mari, Thomas R. Bromley, Nathan Killoran

For a large class of variational quantum circuits, we show how arbitrary-order derivatives can be analytically evaluated in terms of simple parameter-shift rules, i. e., by running the same circuit with different shifts of the parameters.

Quantum Physics

Quantum embeddings for machine learning

no code implementations10 Jan 2020 Seth Lloyd, Maria Schuld, Aroosa Ijaz, Josh Izaac, Nathan Killoran

Quantum classifiers are trainable quantum circuits used as machine learning models.

Quantum Physics

Transfer learning in hybrid classical-quantum neural networks

4 code implementations17 Dec 2019 Andrea Mari, Thomas R. Bromley, Josh Izaac, Maria Schuld, Nathan Killoran

We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements.

Transfer Learning

Quantum Natural Gradient

1 code implementation4 Sep 2019 James Stokes, Josh Izaac, Nathan Killoran, Giuseppe Carleo

A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits.

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

Evaluating analytic gradients on quantum hardware

no code implementations27 Nov 2018 Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, Nathan Killoran

An important application for near-term quantum computing lies in optimization tasks, with applications ranging from quantum chemistry and drug discovery to machine learning.

Quantum Physics

Machine learning method for state preparation and gate synthesis on photonic quantum computers

3 code implementations27 Jul 2018 Juan Miguel Arrazola, Thomas R. Bromley, Josh Izaac, Casey R. Myers, Kamil Brádler, Nathan Killoran

In the simplest case of a single input state, our method discovers circuits for preparing a desired quantum state.

Quantum Physics

Continuous-variable quantum neural networks

8 code implementations18 Jun 2018 Nathan Killoran, Thomas R. Bromley, Juan Miguel Arrazola, Maria Schuld, Nicolás Quesada, Seth Lloyd

The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field.

Fraud Detection

Quantum generative adversarial networks

no code implementations23 Apr 2018 Pierre-Luc Dallaire-Demers, Nathan Killoran

Quantum machine learning is expected to be one of the first potential general-purpose applications of near-term quantum devices.

Machine Learning

Strawberry Fields: A Software Platform for Photonic Quantum Computing

8 code implementations9 Apr 2018 Nathan Killoran, Josh Izaac, Nicolás Quesada, Ville Bergholm, Matthew Amy, Christian Weedbrook

We introduce Strawberry Fields, an open-source quantum programming architecture for light-based quantum computers.

Quantum Physics Computational Physics

Quantum machine learning in feature Hilbert spaces

no code implementations19 Mar 2018 Maria Schuld, Nathan Killoran

We interpret the process of encoding inputs in a quantum state as a nonlinear feature map that maps data to quantum Hilbert space.

Quantum Physics

Generating and designing DNA with deep generative models

2 code implementations17 Dec 2017 Nathan Killoran, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey

We propose generative neural network methods to generate DNA sequences and tune them to have desired properties.

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