2 citations · 3 across the 3 of their papers we have counts for
3 papers
quant-ph2022
Deterministic Tensor Network Classifiers
L. Wright, F. Barratt, J. Dborin +3
We present tensor networks for feature extraction and refinement of classifier performance. These networks can be initialised deterministically and have the potential for implement…
cs.LG2022★ 2 cited
Improvements to Gradient Descent Methods for Quantum Tensor Network Machine Learning
Fergus Barratt, James Dborin, Lewis Wright
Tensor networks have demonstrated significant value for machine learning in a myriad of different applications. However, optimizing tensor networks using standard gradient descent…
quant-ph2021★ 1 cited
Matrix Product State Pre-Training for Quantum Machine Learning
James Dborin, Fergus Barratt, Vinul Wimalaweera +2
Hybrid Quantum-Classical algorithms are a promising candidate for developing uses for NISQ devices. In particular, Parametrised Quantum Circuits (PQCs) paired with classical optimi…