Abstract
We propose a method to build quantum memristors in quantum photonic platforms. We firstly design an effective beam splitter, which is tunable in real-time, by means of a Mach-Zehnder-type array with two equal 50:50 beam splitters and a tunable retarder, which allows us to control its reflectivity. Then, we show that this tunable beam splitter, when equipped with weak measurements and classical feedback, behaves as a quantum memristor. Indeed, in order to prove its quantumness, we show how to codify quantum information in the coherent beams. Moreover, we estimate the memory capability of the quantum memristor. Finally, we show the feasibility of the proposed setup in integrated quantum photonics.
Highlights
Circuit elements whose dynamics intrinsically depends on their past evolution[1,2,3] promise to induce a novel approach in information processing and neuromorphic computing[4,5,6,7] due to their passive storage capabilities
By showing that the fundamental elements which constitute a quantum memristor, namely, a tunable dissipative element, weak measurements, and classical feedback,[15] can be straightforwardly constructed in quantum photonics, we study the dynamics of different initial quantum states and demonstrate the presence of prototypical hysteresis loops
By using the fundamental elements for the quantization of a memristor, namely, a tunable dissipative environment, weak measurements, and classical feedback, we have extended the concept of quantum memristors from superconducting circuits to quantum photonics, showing that all these elements are present in current technology
Summary
Circuit elements whose dynamics intrinsically depends on their past evolution[1,2,3] promise to induce a novel approach in information processing and neuromorphic computing[4,5,6,7] due to their passive storage capabilities. (1b) where the state-variable dynamics, encoded in the real-valued function G(μ(t), V (t)), and the statevariable-dependent conductance function F(μ(t)) > 0 lead to a characteristic pinched hysteresis loop of a memristor when a periodic driving is applied.[8] Memristor technology is currently a promising paradigm to replace the medium term computing architectures based on transistors for certain specific tasks because of the low energy consumption.[9] they are energetically more efficient,[10] and the presence of memory seems to make them more powerful for key machine learning tasks, such as image recognition.[9,11,12,13,14] The quantization of these devices with memory, especially the memristor, is a complicated challenge which has only recently been achieved.[15,16,17] The difficulty lies on the fact that it is necessary to engineer an open quantum system whose classical limit corresponds to the general dynamics given by Eqs.
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