06/07/2016, 10:00 — 11:00 — Room P3.10, Mathematics Building
Mikel Sanz, University of the Basque Country
From Quantum Memristors to Neuromorphic Quantum Computing
Technology based on memristors, resistors with memory whose resistance depends on the hist ory of the crossing charges, has lately enhanced the classical paradigm of computation with neuromorphic architectures. However, in contrast to the known quantized models of passive circuit elements, such as inductors, capacitors or resistors, the design and realization of a quantum memristor was still missing. Here, we introduce the concept of a quantum memristor as a quantum dissipative device, whose decoherence mechanism is controlled by a continuous-measurement feedback scheme, which accounts for the memory. Indeed, we provide numerical simulations showing that memory effects actually persist in the quantum regime. Our quantization method, specifically designed for superconducting circuits, may be extended to other quantum platforms, allowing for memristor-type constructions in different quantum technologies. The proposed quantum memristor is then, in the framework of quantum biomimetics, a building block for quantum neural network, quantum machine learning, quantum simul ations of non-Markovian systems and, in the long term, neuromorphic quantum computation.
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