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Found 9 papers, 2 papers with code
On fundamental aspects of quantum extreme learning machines
Notably, the expressivity of QELMs is fundamentally limited by the number of Fourier frequencies and the number of observables, while the complexity of the prediction hinges on the reservoir.
Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics
This is in contrast to the conventional application of reservoir computing that concentrates on the prediction of the dynamics of observables.
Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling
The energy differences between these structures and the true ground states are, on average, 68. 1 meV/atom lower than those generated by the original CDVAE.
StrainTensorNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks
Accurately predicting the elastic properties of crystalline solids is vital for computational materials science.
Explainable Natural Language Processing with Matrix Product States
Despite empirical successes of recurrent neural networks (RNNs) in natural language processing (NLP), theoretical understanding of RNNs is still limited due to intrinsically complex non-linear computations.
Quantum diffusion map for nonlinear dimensionality reduction
Inspired by random walk on graphs, diffusion map (DM) is a class of unsupervised machine learning that offers automatic identification of low-dimensional data structure hidden in a high-dimensional dataset.
Follow-the-Regularized-Leader Routes to Chaos in Routing Games
We establish that, even in simple linear non-atomic congestion games with two parallel links and any fixed learning rate, unless the game is fully symmetric, increasing the population size or the scale of costs causes learning dynamics to become unstable and eventually chaotic, in the sense of Li-Yorke and positive topological entropy.
Biologically Plausible Sequence Learning with Spiking Neural Networks
Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (MPN), for sequence learning in spiking neural networks.
Mean Field Theory of Activation Functions in Deep Neural Networks
We present a Statistical Mechanics (SM) model of deep neural networks, connecting the energy-based and the feed forward networks (FFN) approach.
