PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantum-classical computations.
Topological error correcting codes, and particularly the surface code, currently provide the most feasible roadmap towards large-scale fault-tolerant quantum computation.
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.
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.
More than 50 years ago Bongard introduced 100 visual concept learning problems as a testbed for intelligent vision systems.
There is large consent that successful training of deep networks requires many thousand annotated training samples.
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We present a framework for experimenting with secure multi-party computation directly in TensorFlow.
TensorNetwork is an open source library for implementing tensor network algorithms in TensorFlow.
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.