Quantum Memristors

Abstract

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.

References in corpus (8)

• A Straightforward Introduction to Continuous Quantum Measurement
• Digital quantum simulation of fermionic models with a superconducting circuit
• Detecting bit-flip errors in a logical qubit using stabilizer measurements
• Digital quantum simulation of spin models with circuit quantum electrodynamics
• Quantum Memristors with Superconducting Circuits
• Memcomputing NP-complete problems in polynomial time using polynomial resources and collective states
• Fundamental Speed Limits to the Generation of Quantumness
• Qubit-based memcapacitors and meminductors

Cited by in corpus (41)

• Experimental quantum memristor
• Quantum neuromorphic computing
• Basic protocols in quantum reinforcement learning with superconducting circuits
• Quantum Artificial Life in an IBM Quantum Computer
• Polynomial-time solution of prime factorization and NP-hard problems with digital memcomputing machines
• Quantum Memristors with Superconducting Circuits
• Quantum autoencoders via quantum adders with genetic algorithms
• Quantum Memristors in Quantum Photonics
• Digital quantum simulation of many-body non-Markovian dynamics
• Quantum machine learning and quantum biomimetics: A perspective
• Lévy noise effects on Josephson junctions
• Multiqubit and multilevel quantum reinforcement learning with quantum technologies
• Perceptrons from Memristors
• Hysteretic superconducting heat-flux quantum modulator
• Analog simulator of integro-differential equations with classical memristors
• Retrieving Quantum Information with Active Learning
• Quantum Memristors in Frequency-Entangled Optical Fields
• A double quantum dot memristor
• Quantum chemistry and charge transport in biomolecules with superconducting circuits
• Entangled Quantum Memristors
• Benefits of Open Quantum Systems for Quantum Machine Learning
• Tripartite entanglement in quantum memristors
• Polariton-based quantum memristors
• Advanced-Retarded Differential Equations in Quantum Photonic Systems
• Thermometry and memcapacitance with qubit-resonator system
• Simulation of memristive synapses and neuromorphic computing on a quantum computer
• Quantized Three-Ion-Channel Neuron Model for Neural Action Potentials
• Quantized Single-Ion-Channel Hodgkin-Huxley Model for Quantum Neurons
• The mise en scene of memristive networks: effective memory, dynamics and learning
• Quantum Memristors with Quantum Computers
• A Quantum Leaky Integrate-and-Fire Spiking Neuron and Network
• Quantum Brain Networks: a Perspective
• Tunable Non-Markovianity for Bosonic Quantum Memristors
• Algorithmic quantum simulation of memory effects
• Microwave Quantum Memcapacitor Effect
• Protected State Transfer via an Approximate Quantum Adder
• Dynamic Behaviors and Training Effects in TiN/Ti/HfO${}_x$/TiN Nanolayered Memristors with Controllable Quantized Conductance States: Implications for Quantum and Neuromorphic Computing Devices
• Can biological quantum networks solve NP-hard problems?
• Machine Learning for maximizing the memristivity of single and coupled quantum memristors
• Quantum memristor with vacuum--one-photon qubits
• Quantum Advantage with Timestamp Membosonsampling