most citedImprovements to Gradient Descent Methods for Quantum Tensor Network Machine Learning

2 citations · 3 across the 3 of their papers we have counts for

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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…

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…