Neuromorphic Quantum Computing and Quantum Machine Learning
In 2008, the emergence of the first experimental realization of a memristor, a resistor with memory whose resistance depends on the history of charges crossing the device, meant a technological revolution which promises to radically transform our computational framework. In this context, we wondered how to properly quantize this device in such a way that it is able to deal with quantum information. We are also interested in the possible applications of these quantum memristors not only to quantum computing, what we call neuromorphic quantum computing (NQC), but also to quantum simulations of non-Markovian, fluid dynamics and, especially, to the implementation of Quantum Machine Learning (QML) protocols. Indeed, a device with memory seems a natural platform to implement QML protocols. We are currently working on implementing quantum memristors in different quantum platforms, quantifying their memory in the framework of non-Markovian processes, and studying how to engineer this memory for implementing QML algorithms.
– Quantum memristors
– Dissipative and non-dissipative memory elements
– Memory quantification and hysteresis processes
– Quantum machine learning protocols
– Neuromorphic quantum computing
– Implementation of quantum memristors in quantum technologies
Pictorial view of a quantum memristor.