Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:1603.04487 · Scientific Reports

Quantum Memristors with Superconducting Circuits

Juha Salmilehto, Frank Deppe, Massimiliano Di Ventra, Mikel Sanz, E. Solano

Published Feb 14, 2017• 80 citations
arXivHTMLPDF

Abstract

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.

DOI
10.1038/srep42044

Related papers

Hybrid superconducting-ferroelectric quantum memristor

Maria A. Badarne, Emanuele G. Dalla Torre, Yachin Ivry

The nontrivial integration of qubits with classical computing systems and the lack of quantum-memory devices remain a key obstacle in the development of quantum technologies. Here, we present a theoretical platform for hybrid quantum devices with memory characteristics. In these current-driven hybrid memristors, a ferroelectric layer serves as the tunneling barrier in a superconducting Josephson junction; thus, the potential barrier is history-dependent. A memory-integrated phase qubit and a Lagrangian description of the superconducting circuit are also presented, paving the way to highly functional, self-modulated Josephson-junction technologies.

Physical Review Research•Nov 4, 2025•3 citations

Quantum memristors

Paul E. Pfeiffer, I. L. Egusquiza, Massimiliano Di Ventra +2 more

Technology based on memristors, resistors with memory whose resistance depends on the history 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 is 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 a building block for neuromorphic quantum computation and quantum simulations of non-Markovian systems.

arXiv:1511.02192•Scientific Reports•Jul 6, 2016•86 citations

Entangled quantum memristors

Shubham Kumar, F. A. Cárdenas-López, Narendra N. Hegade +4 more

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 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 noisy intermediate-scale quantum era technologies.

arXiv:2107.05306•Physical Review A•Dec 6, 2021•17 citations

Quantum Memristor

Alberto Delgado

Research in quantum computing hardware has shown that quantum bits can be implemented with superconducting circuits, these qubits could be used in other applications such as quantum devices for electronic systems. In this paper a nonlinear parameterised two qubits quantum device is presented, the current - voltage characteristic of this element is similar to an adjustable memristor when its parameters are changed.

•Jun 18, 2020•2 citations

Quantum Memristor

Alberto Delgado

Research in quantum computing hardware has shown that quantum bits can be implemented with superconducting circuits, these qubits could be used in other applications such as quantum devices for electronic systems. In this paper a nonlinear parameterised two qubits quantum device is presented, the current - voltage characteristic of this element is similar to an adjustable memristor when its parameters are changed.

•Jun 18, 2020•0 citations

Proposal for Trapped-Ion Quantum Memristor

Sergey Yu Stremoukhov, Форш Павел Анатольевич, Ksenia Yu. Khabarova +1 more

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".

Entropy•Jul 28, 2023•19 citations
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.
The nontrivial integration of qubits with classical computing systems and the lack of quantum-memory devices remain a key obstacle in the development of quantum technologies. Here, we present a theoretical platform for hybrid quantum devices with memory characteristics. In these current-driven hybrid memristors, a ferroelectric layer serves as the tunneling barrier in a superconducting Josephson junction; thus, the potential barrier is history-dependent. A memory-integrated phase qubit and a Lagrangian description of the superconducting circuit are also presented, paving the way to highly functional, self-modulated Josephson-junction technologies.
Technology based on memristors, resistors with memory whose resistance depends on the history 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 is 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 a building block for neuromorphic quantum computation and quantum simulations of non-Markovian systems.
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 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 noisy intermediate-scale quantum era technologies.
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".