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Found 7 papers, 0 papers with code
Structure optimization for parameterized quantum circuits
We demonstrate the method for optimizing a variational quantum eigensolver for finding the ground states of Lithium Hydride and the Heisenberg model in simulation, and for finding the ground state of Hydrogen gas on the IBM Melbourne quantum computer.
Quantum Physics
Adversarial quantum circuit learning for pure state approximation
Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data.
Hierarchical quantum classifiers
Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state.
Quantum Physics
Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz
This paper demonstrates a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA).
Learning hard quantum distributions with variational autoencoders
This suggests that the probability distributions associated to hard quantum states might have a compositional structure that can be exploited by layered neural networks.
Deep disentangled representations for volumetric reconstruction
We introduce a convolutional neural network for inferring a compact disentangled graphical description of objects from 2D images that can be used for volumetric reconstruction.
Predicting and visualizing psychological attributions with a deep neural network
Judgments about personality based on facial appearance are strong effectors in social decision making, and are known to have impact on areas from presidential elections to jury decisions.
