PennyLane: Automatic differentiation of hybrid quantum-classical computations

12 Nov 20187 code implementations

PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantum-classical computations.


Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation

16 Oct 20181 code implementation

Topological error correcting codes, and particularly the surface code, currently provide the most feasible roadmap towards large-scale fault-tolerant quantum computation.

Quantum Neuron: an elementary building block for machine learning on quantum computers

30 Nov 20171 code implementation

In the construction of feedforward networks of quantum neurons, we provide numerical evidence that the network not only can learn a function when trained with superposition of inputs and the corresponding output, but that this training suffices to learn the function on all individual inputs separately.


Continuous-variable quantum neural networks

18 Jun 20184 code implementations

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.


Solving Bongard Problems with a Visual Language and Pragmatic Reasoning

12 Apr 20181 code implementation

More than 50 years ago Bongard introduced 100 visual concept learning problems as a testbed for intelligent vision systems.


Private Machine Learning in TensorFlow using Secure Computation

18 Oct 20182 code implementations

We present a framework for experimenting with secure multi-party computation directly in TensorFlow.

TensorNetwork on TensorFlow: A Spin Chain Application Using Tree Tensor Networks

3 May 20192 code implementations

TensorNetwork is an open source library for implementing tensor network algorithms in TensorFlow.


Differentiable Learning of Quantum Circuit Born Machine

11 Apr 20184 code implementations

However, similar to the leading implicit generative models in deep learning, such as the generative adversarial networks, the quantum circuits cannot provide the likelihood of the generated samples, which poses a challenge to the training.

Parameterized quantum circuits as machine learning models

18 Jun 20194 code implementations

Hybrid quantum-classical systems make it possible to utilize existing quantum computers to their fullest extent.