"While entanglement is necessary for quantum speedups, the research indicates the need for careful design of which qubits should be entangled and how much," says research co-author Dr Maria Kieferova, Research Fellow at the ARC Centre for Quantum Computation and Communication Technology based at the University of Technology Sydney.
"This is in contradiction to the common understanding that more quantum entanglement provides faster speedups.'
"We have proven that excess entanglement between the output qubits, or visible units, and the rest of the quantum neural network hinders the learning process and that large amounts of entanglement can be catastrophic for the model," says lead author Dr Carlos Ortiz Marrero, who is currently a Research Assistant Professor at North Carolina State University.
"This result teaches us which structures of quantum neural networks we need to avoid for successful algorithms."
"Even though the research showed that a range of straightforward translations from classical machine learning models to the quantum realm isn't beneficial, there is a way forward," says Dr Ortiz Marrero.
"By limiting the depth and connectivity of the network, we might be able to avoid the regimes where quantum machine learning algorithms cannot be trained."
This can be achieved by precisely and deliberately deploying entanglement in quantum machine learning models.
"While entanglement is a powerful tool to add to our models, it must be used like a scalpel and not a sledgehammer," says co-author Dr Nathan Wiebe, University of Toronto.