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Found 11 papers, 9 papers with code
Quantum Equilibrium Propagation for efficient training of quantum systems based on Onsager reciprocity
The widespread adoption of machine learning and artificial intelligence in all branches of science and technology has created a need for energy-efficient, alternative hardware platforms.
Training of Physical Neural Networks
Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems.
Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning
Their optimal policy typically maximizes the expected sum of rewards given at each step of the decision process.
Training Coupled Phase Oscillators as a Neuromorphic Platform using Equilibrium Propagation
Given the rapidly growing scale and resource requirements of machine learning applications, the idea of building more efficient learning machines much closer to the laws of physics is an attractive proposition.
Optimizing ZX-Diagrams with Deep Reinforcement Learning
ZX-diagrams are a powerful graphical language for the description of quantum processes with applications in fundamental quantum mechanics, quantum circuit optimization, tensor network simulation, and many more.
Deep Bayesian Experimental Design for Quantum Many-Body Systems
Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain.
Investigation of inverse design of multilayer thin-films with conditional invertible Neural Networks
The task of designing optical multilayer thin-films regarding a given target is currently solved using gradient-based optimization in conjunction with methods that can introduce additional thin-film layers.
Artificial Intelligence and Machine Learning for Quantum Technologies
In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly.
TMM-Fast: A Transfer Matrix Computation Package for Multilayer Thin-Film Optimization
Achieving the desired optical response from a multilayer thin-film structure over a broad range of wavelengths and angles of incidence can be challenging.
Self-learning Machines based on Hamiltonian Echo Backpropagation
A physical self-learning machine can be defined as a nonlinear dynamical system that can be trained on data (similar to artificial neural networks), but where the update of the internal degrees of freedom that serve as learnable parameters happens autonomously.
Renormalized Mutual Information for Artificial Scientific Discovery
We derive a well-defined renormalized version of mutual information that allows to estimate the dependence between continuous random variables in the important case when one is deterministically dependent on the other.
