Advances in quantum machine learning
read the original abstract
Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion. The field's outlook is generally positive, showing significant promise. However, we believe there are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation.
This paper has not been read by Pith yet.
Forward citations
Cited by 2 Pith papers
-
Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning
Meta-learning with 24 classical complexity metrics predicts the optimal quantum encoding circuit among 9 candidates with up to 85.7% top-3 accuracy.
-
Machine learning methods in quantum computing theory
Authors present a multiclass tree tensor network algorithm demonstrated on IBM quantum processor and a neural network approach for noise-robust quantum state tomography.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.