Quantum Memristor Research Articles

Hysteresis and self-oscillations in an artificial memristive quantum neuron

We theoretically study an artificial neuron circuit containing a quantum memristor in the presence of relaxation and dephasing. The charge transport in the quantum element is realized via tunneling of a charge through a quantum particle which shuttles between two terminals—a functionality reminiscent of classical diffusive memristors. We demonstrate that this physical principle enables hysteretic behavior in the current-voltage characteristics of the quantum device. In addition, being used in an artificial neural circuit, the quantum switcher is able to generate self-sustained current oscillations. Our analysis reveals that these self-oscillations are triggered only in quantum regimes with a moderate rate of relaxation, and cannot exist either in a purely coherent regime or at a very high decoherence. We investigate the hysteresis and instability leading to the onset of current self-oscillations and analyze their properties depending on the circuit parameters. Our results provide a generic approach to the use of quantum regimes for controlling hysteresis and generating self-oscillations. Published by the American Physical Society 2024

Machine Learning for Maximizing the Memristivity of Single and Coupled Quantum Memristors

AbstractMachine learning (ML) methods are proposed to characterize the memristive properties of single and coupled quantum memristors. It is shown 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. The results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.

Model of Coupled Quantum Memristors Based on a Single Trapped 171Yb+ Ion

A method for the joint application of two coupled quantum memristors on a single 171Yb+ ion by using optical and radio-frequency transitions induced by resonant laser fields is proposed. As a result, coherent mapping of the input quantum state to the output state is created. The proposed method makes it possible to use only one ion to control statistical weights in two-layer perceptrons.

Proposal for Trapped-Ion Quantum Memristor.

A quantum memristor combines the memristive dynamics with the quantum behavior of the system. We analyze the idea of a quantum memristor based on ultracold ions trapped in a Paul trap. Corresponding input and output memristor signals are the ion electronic levels populations. We show that under certain conditions the output/input dependence is a hysteresis curve similar to classical memristive devices. This behavior becomes possible due to the partial decoherence provided by the feedback loop, which action depends on previous state of the system (memory). The feedback loop also introduces nonlinearity in the system. Ion-based quantum memristor possesses several advantages comparing to other platforms-photonic and superconducting circuits-due to the presence of a large number of electronic levels with different lifetimes as well as strong Coulomb coupling between ions in the trap. The implementation of the proposed ion-based quantum memristor will be a significant contribution to the novel direction of "quantum neural networks".

Tunable Non-Markovianity for Bosonic Quantum Memristors.

We studied the tunable control of the non-Markovianity of a bosonic mode due to its coupling to a set of auxiliary qubits, both embedded in a thermal reservoir. Specifically, we considered a single cavity mode coupled to auxiliary qubits described by the Tavis-Cummings model. As a figure of merit, we define the dynamical non-Markovianity as the tendency of a system to return to its initial state, instead of evolving monotonically to its steady state. We studied how this dynamical non-Markovianity can be manipulated in terms of the qubit frequency. We found that the control of the auxiliary systems affects the cavity dynamics as an effective time-dependent decay rate. Finally, we show how this tunable time-dependent decay rate can be tuned to engineer bosonic quantum memristors, involving memory effects that are fundamental for developing neuromorphic quantum technologies.

Mimicking bacterial learning and memory in tungsten based two-sided single layers of WSeO, WSeS, WSeSe, and WSeTe

We mimic bacterial learning and memory abilities in tungsten based two-sided single layers of WSeO, WSeS, WSeSe, and WSeTe, where the thickness of the material represents the growth in time. We aim to create a quantum memristor like system to show learning and memory abilities of bacteria with time while it grows. Its governing equation is derived, and it was found to be similar to the gene regulatory response model of the bacteria. Polarization is calculated from the Berry phase theory to plot its relation with the degauss parameter in time, which leads to bow-tie like memory switches similar to phase-change memories. We attribute this behavior to a specific bacteria, that is, Geobacter metallireducens. Mimicking bacteria’s learning and memory abilities will open a way to merge physical intelligence with quantum computing computationally.

Quantum Machine Learning Implementations: Proposals and Experiments

AbstractThis article gives an overview and a perspective of recent theoretical proposals and their experimental implementations in the field of quantum machine learning. Without an aim to being exhaustive, the article reviews specific high‐impact topics such as quantum reinforcement learning, quantum autoencoders, and quantum memristors, and their experimental realizations in the platforms of quantum photonics and superconducting circuits. The field of quantum machine learning can be among the first quantum technologies producing results that are beneficial for industry and, in turn, to society. Therefore, it is necessary to push forward initial quantum implementations of this technology, in noisy intermediate‐scale quantum computers, aiming for achieving fruitful calculations in machine learning that are better than with any other current or future computing paradigm.

Interface-Assisted Room-Temperature Magnetoresistance in Cu-Phenalenyl-Based Magnetic Tunnel Junctions

Delocalized carbon-based radical species with unpaired spin, such as the phenalenyl (PLY) radical, have opened avenues for developing multifunctional organic spintronic devices. Using direct laser writing and in situ deposition, we successfully fabricated Cu-PLY- and Zn-PLY-based organic magnetic tunnel junctions (OMTJs) with improved morphology and a reduced junction area of 3 × 8 μm2. The nonlinear and weakly temperature-dependent current–voltage (I–V) characteristics in combination with the low organic barrier height suggest tunneling as the dominant transport mechanism in the structurally and dimensionally optimized OMTJs. Cu-PLY-based OMTJs show significant magnetoresistance up to 14% at room temperature due to the formation of hybrid states at the metal–molecule interfaces called “spinterface”, which reveals the importance of spin-dependent interfacial modification in OMTJs’ design. Additionally, at high bias, in the absence of a magnetic field, OMTJ shows stable voltage-driven resistive switching. Cu-PLY having spin 1/2 with net magnetic moment demonstrates magnetic hardening between the surface molecule at the Co interface and gives rise to stable MR, which suggests its use as a feasible and scalable platform for building molecular-scale quantum memristors and processors.

A method to detect quantum coherent transport in memristive devices

While the size of functional elements in memristors becomes of the orders of nano-meters or even smaller, the quantum effects in their dynamics can significantly influence their transport properties, consistent with recent experimental observations of conductance quantisation in memristors. This requires the development of experimentally accessible signatures of quantum behaviour in memristive systems, such as a superposition of quantum states with different memristances. Here we discuss one such protocol. Our simulations show that periodic projective measurements induce additional spectral components in the response of quantum memristor to a harmonic input signal. Moreover, the response demonstrates a resonant behaviour when the frequency of the projective measurements commensurates with the frequency of the input. We demonstrate that observation of such harmonic mixing can be used as experimental evidence of quantum effects in memristors.Graphic abstract

Tripartite Entanglement in Quantum Memristors

We study the entanglement and memristive properties of three coupled quantum memristors. We consider quantum memristors based on superconducting asymmetric SQUID architectures which are coupled via inductors. The three quantum memristors are arranged in two different geometries: linear and triangular coupling configurations. We obtain a variety of correlation measures, including bipartite entanglement and tripartite negativity. We find that, for identical quantum memristors, entanglement and memristivity follow the same behavior for the triangular case and the opposite one in the linear case. Finally, we study the multipartite correlations with the tripartite negativity and entanglement monogamy relations, showing that our system has genuine tripartite entanglement. Our results show that quantum correlations in multipartite memristive systems have a non-trivial role and can be used to design quantum memristor arrays for quantum neural networks and neuromorphic quantum computing architectures.

Quantum Memristors with Quantum Computers

We propose the encoding of memristive quantum dynamics on a digital quantum computer. Using a set of auxiliary qubits, we simulate an effective non-Markovian environment inspired by a collisional model, reproducing memristive features between expectation values of different operators in a single qubit. We numerically test our proposal in an IBM quantum simulator with 32 qubits, obtaining a pinched hysteresis curve that characterizes our quantum memristor model. Furthermore, we extend our method to the case of two coupled quantum memristors, opening the door to the study of neuromorphic quantum computing in the current noisy devices.

Experimental photonic quantum memristor

Memristive devices are a class of physical systems with history-dependent dynamics characterized by signature hysteresis loops in their input–output relations. In the past few decades, memristive devices have attracted enormous interest in electronics. This is because memristive dynamics is very pervasive in nanoscale devices, and has potentially groundbreaking applications ranging from energy-efficient memories to physical neural networks and neuromorphic computing platforms. Recently, the concept of a quantum memristor was introduced by a few proposals, all of which face limited technological practicality. Here we propose and experimentally demonstrate a novel quantum-optical memristor (based on integrated photonics) that acts on single-photon states. We fully characterize the memristive dynamics of our device and tomographically reconstruct its quantum output state. Finally, we propose a possible application of our device in the framework of quantum machine learning through a scheme of quantum reservoir computing, which we apply to classical and quantum learning tasks. Our simulations show promising results, and may break new ground towards the use of quantum memristors in quantum neuromorphic architectures.

Artificial neural network modeling and control for dynamical and statistical characteristics of photonic quantum memristor

Artificial neural network modeling and control for dynamical and statistical characteristics of photonic quantum memristor

Polariton-Based Quantum Memristors

Information processing and storage by the same physical system is emerging as a promising alternative to traditional computing platforms. In turn, this requires the realization of elementary units the memory content of which can be easily tuned and controlled. Here, we introduce a polariton-based quantum memristor where the memristive nature arises from the intercavity polariton exchange and is controlled by a time-varying atom-cavity detuning. A dynamical hysteresis is characterized by the fluctuations in the instantaneous polariton number, where the history information is encoded into a dynamical phase. Using a Lindblad master-equation approach, we find that features of the quantum memristor dynamics, such as the area and circulation of the hysteresis loop, showcase a kind of ``plasticity'' controlled by quantum state initialization. This makes this quantum memristor very versatile for a wide range of applications.

Entangled quantum memristors

We propose the interaction of two quantum memristors via capacitive and inductive coupling in feasible superconducting circuit architectures. In this composed system the input gets correlated in time, which changes the dynamic response of each quantum memristor in terms of its pinched hysteresis curve and their nontrivial entanglement. In this sense, the concurrence and memristive dynamics follow an inverse behavior, showing maximal values of entanglement when the hysteresis curve is minimal and vice versa. Moreover, the direction followed in time by the hysteresis curve is reversed whenever the quantum memristor entanglement is maximal. The study of composed quantum memristors paves the way for developing neuromorphic quantum computers and native quantum neural networks, on the path towards quantum advantage with current NISQ technologies.

Quantum Memristors in Frequency-Entangled Optical Fields.

A quantum memristor is a passive resistive circuit element with memory, engineered in a given quantum platform. It can be represented by a quantum system coupled to a dissipative environment, in which a system–bath coupling is mediated through a weak measurement scheme and classical feedback on the system. In quantum photonics, such a device can be designed from a beam splitter with tunable reflectivity, which is modified depending on the results of measurements in one of the outgoing beams. Here, we show that a similar implementation can be achieved with frequency-entangled optical fields and a frequency mixer that, working similarly to a beam splitter, produces state superpositions. We show that the characteristic hysteretic behavior of memristors can be reproduced when analyzing the response of the system with respect to the control, for different experimentally attainable states. Since memory effects in memristors can be exploited for classical and neuromorphic computation, the results presented in this work could be a building block for constructing quantum neural networks in quantum photonics, when scaling up.

Quantized Single-Ion-Channel Hodgkin-Huxley Model for Quantum Neurons

The Hodgkin-Huxley model describes the behavior of the cell membrane in neurons, treating each part of it as an electric circuit element, namely capacitors, memristors, and voltage sources. We focus on the activation channel of potassium ions, due to its simplicity, while keeping most of the features displayed by the original model. This reduced version is essentially a classical memristor, a resistor whose resistance depends on the history of electric signals that have crossed it, coupled to a voltage source and a capacitor. Here, we will consider a quantized Hodgkin-Huxley model based on a quantum memristor formalism. We compare the behavior of the membrane voltage and the potassium channel conductance, when the circuit is subjected to AC sources, in both classical and quantum realms. Numerical simulations show an expected adaptation of the considered channel conductance depending on the signal history in all regimes. Remarkably, the computation of higher moments of the voltage manifest purely quantum features related to the circuit zero-point energy. Finally, we study the implementation of the Hodgkin-Huxley quantum memristor as an asymmetric rf SQUID in superconducting circuits. This study may allow the construction of quantum neuron networks inspired in the brain function, as well as the design of neuromorphic quantum architectures for quantum machine learning.

Invited Article: Quantum memristors in quantum photonics

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.

Double quantum dot memristor

Memristive systems are generalisations of memristors, which are resistors with memory. In this paper, we present a quantum description of memristive systems. Using this model we propose and experimentally demonstrate a simple and practical scheme for realising memristive systems with quantum dots. The approach harnesses a phenomenon that is commonly seen as a bane of nanoelectronics, i.e. switching of a trapped charge in the vicinity of the device. We show that quantum-dot memristive systems have hysteresis current-voltage characteristics and quantum jump induced stochastic behaviour. Realising such a quantum memristor completes the menu of components for quantum circuit design.

Quantum Memristors with Superconducting Circuits

Memristors are resistive elements retaining information of their past dynamics. They have garnered substantial interest due to their potential for representing a paradigm change in electronics, information processing and unconventional computing. Given the advent of quantum technologies, a design for a quantum memristor with superconducting circuits may be envisaged. Along these lines, we introduce such a quantum device whose memristive behavior arises from quasiparticle-induced tunneling when supercurrents are cancelled. For realistic parameters, we find that the relevant hysteretic behavior may be observed using current state-of-the-art measurements of the phase-driven tunneling current. Finally, we develop suitable methods to quantify memory retention in the system.