* Phys. Rev. A 98, 012324 (2018)

* 10 pages, 8 figures

**Click to Read Paper**

PennyLane: Automatic differentiation of hybrid quantum-classical computations

Nov 12, 2018

Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, Nathan Killoran

Nov 12, 2018

Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, Nathan Killoran

* Code available at https://github.com/XanaduAI/pennylane/ . Significant contributions to the code (new features, new plugins, etc.) will be recognized by the opportunity to be a co-author on this paper

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Generating and designing DNA with deep generative models

Dec 17, 2017

Nathan Killoran, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey

Dec 17, 2017

Nathan Killoran, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey

* NIPS 2017 Computational Biology Workshop

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Continuous-variable quantum neural networks

Jun 18, 2018

Nathan Killoran, Thomas R. Bromley, Juan Miguel Arrazola, Maria Schuld, Nicolás Quesada, Seth Lloyd

We introduce a general method for building neural networks on quantum computers. 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. This circuit contains a layered structure of continuously parameterized gates which is universal for CV quantum computation. Affine transformations and nonlinear activation functions, two key elements in neural networks, are enacted in the quantum network using Gaussian and non-Gaussian gates, respectively. The non-Gaussian gates provide both the nonlinearity and the universality of the model. Due to the structure of the CV model, the CV quantum neural network can encode highly nonlinear transformations while remaining completely unitary. We show how a classical network can be embedded into the quantum formalism and propose quantum versions of various specialized model such as convolutional, recurrent, and residual networks. Finally, we present numerous modeling experiments built with the Strawberry Fields software library. These experiments, including a classifier for fraud detection, a network which generates Tetris images, and a hybrid classical-quantum autoencoder, demonstrate the capability and adaptability of CV quantum neural networks.
Jun 18, 2018

Nathan Killoran, Thomas R. Bromley, Juan Miguel Arrazola, Maria Schuld, Nicolás Quesada, Seth Lloyd

**Click to Read Paper**