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Entangling Quantum Generative Adversarial Networks
Generative adversarial networks (GANs) are one of the most widely adopted semisupervised and unsupervised machine learning methods for high-definition image, video, and audio generation.
Quantum Physics
Accurate Computation of Quantum Excited States with Neural Networks
We present a variational Monte Carlo algorithm for estimating the lowest excited states of a quantum system which is a natural generalization of the estimation of ground states.
Discovering Quantum Phase Transitions with Fermionic Neural Networks
Deep neural networks have been extremely successful as highly accurate wave function ans\"atze for variational Monte Carlo calculations of molecular ground states.
Continuous-variable quantum neural networks
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.
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.
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Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
Binary neural networks, i. e., neural networks whose parameters and activations are constrained to only two possible values, offer a compelling avenue for the deployment of deep learning models on energy- and memory-limited devices.
Better than classical? The subtle art of benchmarking quantum machine learning models
Benchmarking models via classical simulations is one of the main ways to judge ideas in quantum machine learning before noise-free hardware is available.
Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks
Recurrent neural networks play an important role in both research and industry.
PennyLane: Automatic differentiation of hybrid quantum-classical computations
PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation.
Supervised Learning with Quantum-Inspired Tensor Networks
Tensor networks are efficient representations of high-dimensional tensors which have been very successful for physics and mathematics applications.
