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Found 9 papers, 7 papers with code
Quantum circuit synthesis with diffusion models
The model excels at generating new circuits and supports typical DM extensions such as masking and editing to, for instance, align the circuit generation to the constraints of the targeted quantum device.
Universal representation by Boltzmann machines with Regularised Axons
It is widely known that Boltzmann machines are capable of representing arbitrary probability distributions over the values of their visible neurons, given enough hidden ones.
Learning minimal representations of stochastic processes with variational autoencoders
Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena.
Optimal foraging strategies can be learned
Recognizing the interconnected nature of these challenges, this work addresses them simultaneously by exploring optimal foraging strategies through a reinforcement learning framework.
Preface: Characterisation of Physical Processes from Anomalous Diffusion Data
Preface to the special issue "Characterisation of Physical Processes from Anomalous Diffusion Data" associated with the Anomalous Diffusion Challenge ( https://andi-challenge. org ) and published in Journal of Physics A: Mathematical and Theoretical.
Unsupervised learning of anomalous diffusion data
In this work, we explore the use of unsupervised methods in anomalous diffusion data.
Certificates of quantum many-body properties assisted by machine learning
A number of standard methods are used to tackle such problems: variational approaches focus on parameterizing a subclass of solutions within the feasible set; in contrast, relaxation techniques have been proposed to approximate it from outside, thus complementing the variational approach by providing ultimate bounds to the global optimal solution.
Transfer Learning
Quantum Physics
Efficient training of energy-based models via spin-glass control
We introduce a new family of energy-based probabilistic graphical models for efficient unsupervised learning.
Machine learning method for single trajectory characterization
In this paper, we propose a machine learning method based on a random forest architecture, which is able to associate even very short trajectories to the underlying diffusion mechanism with a high accuracy.
