Machine Learning for maximizing the memristivity of single and coupled quantum memristors
This work addresses the potential use of quantum memristors as components in neuromorphic quantum computing, but it appears incremental as it applies existing ML methods to a new domain.
The authors tackled the problem of characterizing and maximizing memristivity in single and coupled quantum memristors, showing that this leads to large values in entanglement degree, revealing a link between quantum correlations and memory.
We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors. We show that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. Our results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.