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20Quantum memristors as a new stage on the way from quantum to
neuromorphic computing
S. Stremoukhov1'2'3*. P. Forsh12, K. Khabarova1'4' N. Kolachevsky1'4
1-P.N. Lebedev Physical Institute of the Russian Academy of Science, Leninskiy pr., 53, 119991 Moscow, Russia
2- Faculty of Physics, M.V Lomonosov Moscow State University, Leninskie Gory, 1/2, 119991 Moscow, Russia 3- National Research Centre "Kurchatov Institute", Akademika Kurchatova sq. 1, 123182 Moscow, Russia
4- Russian Quantum Center, Bolshoy Bulvar, 30 Bld. 1, 121205 Moscow, Russia
Quantum computing is currently one of the fastest growing topics. To date, the main basic computing tools have been created on four platforms - optical, superconducting, atomic and ionic [1-4]. Scalable quantum computers hold the promise to solve hard computational problems, such as prime factorization, combinatorial optimization, simulation of many-body physics, quantum chemistry as well as simulating the physics of open systems [5]. On the other hand, it has been shown that neuromorphic computing also has a number of advantages due, first of all, to a significant reduction in energy consumption during calculations through the use of one device - a classical memristor - both directly for calculations and information storage [6]. Combining neuromorphic and quantum computing through the use of a quantum memristor as a device that consolidates the ability to store quantum information, as well as quantum parallelism and entanglement, will enable quantum neuromorphic computing. In addition, a classic memristor is close in its properties to a synapse, which ensures contact between neurons in the brain. At the same time, many scientists, in particular Nobel laureate R. Penrose [7], are inclined to believe that the work of the brain is determined by the laws of quantum physics. If this is true, then the quantum memristor and computing systems based on it can more accurately imitate the functioning of the brain.
Quantum memristor concepts have been proposed on superconducting [8], photonic [9], and ionic [10,11] platforms. This paper discusses the features of ionic quantum memristors based on ultracold Yb+ ions. It is shown that at certain values of the parameters of laser pulses, which ensure the movement of the population of selected levels of the ion, a "memristive dependence" of the output signal on the input signal occurs (which is the population of one of the levels at different times, correlated with the action of two laser fields). Two options for creating a quantum memristor have been proposed: on a single ultracold ion and on a chain of ultracold ions connected by a low-frequency vibrational mode of the center of mass. This makes such coupled quantum memristors to be the best candidate for use in neuromorphic computing.
The study was supported by the Russian Science Foundation (Grant № 24-12-00415, https://rscf.ru/project/24-12-00415/).
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