Search Results for author:
Found 5 papers, 0 papers with code
Advantage of Quantum Neural Networks as Quantum Information Decoders
A promising strategy to protect quantum information from noise-induced errors is to encode it into the low-energy states of a topological quantum memory device.
Non-equilibrium physics: from spin glasses to machine and neural learning
Despite the empirical success of utilizing these systems for intelligent tasks, the underlying principles that govern their emergent intelligent behaviors remain largely unknown.
Many-body localized hidden generative models
Born machines are quantum-inspired generative models that leverage the probabilistic nature of quantum states.
A theory of learning with constrained weight-distribution
Our theory predicts that the reduction in capacity due to the constrained weight-distribution is related to the Wasserstein distance between the imposed distribution and that of the standard normal distribution.
A Closer Look at Disentangling in $β$-VAE
In many data analysis tasks, it is beneficial to learn representations where each dimension is statistically independent and thus disentangled from the others.
