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All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization
Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolutionizing the pharmaceuticals and materials industries.
Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines
We argue that this relates to the fact that we are training a quantum rather than classical Boltzmann distribution in this case.
Quantum Boltzmann Machine
Inspired by the success of Boltzmann Machines based on classical Boltzmann distribution, we propose a new machine learning approach based on quantum Boltzmann distribution of a transverse-field Ising Hamiltonian.
Quantum Physics
Understanding Deep Architectures using a Recursive Convolutional Network
A key challenge in designing convolutional network models is sizing them appropriately.
