Passive Components Blog
No Result
View All Result
  • Home
  • NewsFilter
    • All
    • Aerospace & Defence
    • Antenna
    • Applications
    • Automotive
    • Capacitors
    • Circuit Protection Devices
    • electro-mechanical news
    • Filters
    • Fuses
    • Inductors
    • Industrial
    • Integrated Passives
    • inter-connect news
    • Market & Supply Chain
    • Market Insights
    • Medical
    • Modelling and Simulation
    • New Materials & Supply
    • New Technologies
    • Non-linear Passives
    • Oscillators
    • Passive Sensors News
    • Resistors
    • RF & Microwave
    • Telecommunication
    • Weekly Digest

    Panasonic High Precision Chip Resistors Bridge Gap Between Thin and Thick Technology

    Wk 13 Electronics Supply Chain Digest

    Samsung Introduces 35V MLCCs Flying Capacitors for USB PD Fast Charging

    New J‑STD‑075B Standard Elevates Process Sensitivity Classification for Passive and Solid-State Components

    Modelithics Expands COMPLETE+3D Library for Ansys HFSS

    DigiKey Launches “Engineering Unlocked” Video Series

    Equivalent Circuit Constants of Crystal Units Explained

    Vishay Releases Compact High‑Accuracy Hall Effect Linear Position Sensor

    Nanocrystalline Cores for Low‑Loss MHz Chip Inductors

    Trending Tags

    • Ripple Current
    • RF
    • Leakage Current
    • Tantalum vs Ceramic
    • Snubber
    • Low ESR
    • Feedthrough
    • Derating
    • Dielectric Constant
    • New Products
    • Market Reports
  • VideoFilter
    • All
    • Antenna videos
    • Capacitor videos
    • Circuit Protection Video
    • Filter videos
    • Fuse videos
    • Inductor videos
    • Inter-Connect Video
    • Non-linear passives videos
    • Oscillator videos
    • Passive sensors videos
    • Resistor videos

    Transformer-Based Power-Line Harvester Magnetic Design

    Thermal Modeling of Magnetics

    Standard vs Planar LLC transformers Comparison for Battery Chargers

    How Modern Tools Model Magnetic Components for Power Electronics

    Advanced Loss Modeling for Planar Magnetics in the Frenetic Planar Tool

    2026 Power Magnetics Design Trends: Flyback, DAB and Planar

    Enabling Software‑Defined Vehicle Architectures: Automotive Ethernet and Zonal Smart Power

    Calculating Resistance Value of a Flyback RC Snubber 

    One‑Pulse Characterization of Nonlinear Power Inductors

    Trending Tags

    • Capacitors explained
    • Inductors explained
    • Resistors explained
    • Filters explained
    • Application Video Guidelines
    • EMC
    • New Products
    • Ripple Current
    • Simulation
    • Tantalum vs Ceramic
  • Knowledge Blog
  • DossiersNew
  • Suppliers
    • Who is Who
  • PCNS
    • PCNS 2025
    • PCNS 2023
    • PCNS 2021
    • PCNS 2019
    • PCNS 2017
  • Events
  • Home
  • NewsFilter
    • All
    • Aerospace & Defence
    • Antenna
    • Applications
    • Automotive
    • Capacitors
    • Circuit Protection Devices
    • electro-mechanical news
    • Filters
    • Fuses
    • Inductors
    • Industrial
    • Integrated Passives
    • inter-connect news
    • Market & Supply Chain
    • Market Insights
    • Medical
    • Modelling and Simulation
    • New Materials & Supply
    • New Technologies
    • Non-linear Passives
    • Oscillators
    • Passive Sensors News
    • Resistors
    • RF & Microwave
    • Telecommunication
    • Weekly Digest

    Panasonic High Precision Chip Resistors Bridge Gap Between Thin and Thick Technology

    Wk 13 Electronics Supply Chain Digest

    Samsung Introduces 35V MLCCs Flying Capacitors for USB PD Fast Charging

    New J‑STD‑075B Standard Elevates Process Sensitivity Classification for Passive and Solid-State Components

    Modelithics Expands COMPLETE+3D Library for Ansys HFSS

    DigiKey Launches “Engineering Unlocked” Video Series

    Equivalent Circuit Constants of Crystal Units Explained

    Vishay Releases Compact High‑Accuracy Hall Effect Linear Position Sensor

    Nanocrystalline Cores for Low‑Loss MHz Chip Inductors

    Trending Tags

    • Ripple Current
    • RF
    • Leakage Current
    • Tantalum vs Ceramic
    • Snubber
    • Low ESR
    • Feedthrough
    • Derating
    • Dielectric Constant
    • New Products
    • Market Reports
  • VideoFilter
    • All
    • Antenna videos
    • Capacitor videos
    • Circuit Protection Video
    • Filter videos
    • Fuse videos
    • Inductor videos
    • Inter-Connect Video
    • Non-linear passives videos
    • Oscillator videos
    • Passive sensors videos
    • Resistor videos

    Transformer-Based Power-Line Harvester Magnetic Design

    Thermal Modeling of Magnetics

    Standard vs Planar LLC transformers Comparison for Battery Chargers

    How Modern Tools Model Magnetic Components for Power Electronics

    Advanced Loss Modeling for Planar Magnetics in the Frenetic Planar Tool

    2026 Power Magnetics Design Trends: Flyback, DAB and Planar

    Enabling Software‑Defined Vehicle Architectures: Automotive Ethernet and Zonal Smart Power

    Calculating Resistance Value of a Flyback RC Snubber 

    One‑Pulse Characterization of Nonlinear Power Inductors

    Trending Tags

    • Capacitors explained
    • Inductors explained
    • Resistors explained
    • Filters explained
    • Application Video Guidelines
    • EMC
    • New Products
    • Ripple Current
    • Simulation
    • Tantalum vs Ceramic
  • Knowledge Blog
  • DossiersNew
  • Suppliers
    • Who is Who
  • PCNS
    • PCNS 2025
    • PCNS 2023
    • PCNS 2021
    • PCNS 2019
    • PCNS 2017
  • Events
No Result
View All Result
Passive Components Blog
No Result
View All Result

Researchers Demonstrated Quantum Memristor as a Link between AI and Quantum Computing

11.4.2022
Reading Time: 3 mins read
A A

Quantum memristor is a missing link between AI artificial intelligence and quantum computing. Researchers from University if Vienna have now demonstrated this new device.

In recent years, artificial intelligence has become ubiquitous, with applications such as speech interpretation, image recognition, medical diagnosis, and many more. At the same time, quantum technology has been proven capable of computational power well beyond the reach of even the world’s largest supercomputer. Physicists at the University of Vienna have now demonstrated a new device, called quantum memristor, which may allow to combine these two worlds, thus unlocking unprecedented capabilities. The experiment, carried out in collaboration with the National Research Council (CNR) and the Politecnico di Milano in Italy, has been realized on an integrated quantum processor operating on single photons. The work is published in the current issue of the journal “Nature Photonics”.

RelatedPosts

Graphene-based Memristors Show Promise for Brain-Based Computing

Nanometers-thin Niobium Oxide (NbO2) Memristor Can Bring Breakthrough to Neuromorphic AI Hardware Designs

Purity of Materials May be the Key in Further Memristor Development

At the heart of all artificial intelligence applications are mathematical models called neural networks. These models are inspired by the biological structure of the human brain, made of interconnected nodes. Just like our brain learns by constantly rearranging the connections between neurons, neural networks can be mathematically trained by tuning their internal structure until they become capable of human-level tasks: recognizing our face, interpreting medical images for diagnosis, even driving our cars. Having integrated devices capable of performing the computations involved in neural networks quickly and efficiently has thus become a major research focus, both academic and industrial.

One of the major game changers in the field was the discovery of the memristor, made in 2008. This device changes its resistance depending on a memory of the past current, hence the name memory-resistor, or memristor. Immediately after its discovery, scientists realized that (among many other applications) the peculiar behavior of memristors was surprisingly similar to that of neural synapses. The memristor has thus become a fundamental building block of neuromorphic architectures.

A group of experimental physicists from the University of Vienna, the National Research Council (CNR) and the Politecnico di Milano led by Prof. Philip Walther and Dr. Roberto Osellame, have now demonstrated that it is possible to engineer a device that has the same behavior as a memristor, while acting on quantum states and being able to encode and transmit quantum information. In other words, a quantum memristor. Realizing such device is challenging because the dynamics of a memristor tends to contradict the typical quantum behavior.

By using single photons, i.e. single quantum particles of lights, and exploiting their unique ability to propagate simultaneously in a superposition of two or more paths, the physicists have overcome the challenge. In their experiment, single photons propagate along waveguides laser-written on a glass substrate and are guided on a superposition of several paths. One of these paths is used to measure the flux of photons going through the device and this quantity, through a complex electronic feedback scheme, modulates the transmission on the other output, thus achieving the desired memristive behavior. Besides demonstrating the quantum memristor, the researchers have provided simulations showing that optical networks with quantum memristor can be used to learn on both classical and quantum tasks, hinting at the fact that the quantum memristor may be the missing link between artificial intelligence and quantum computing.

“Unlocking the full potential of quantum resources within artificial intelligence is one of the greatest challenges of the current research in quantum physics and computer science”, says Michele Spagnolo, who is first author of the publication in the journal “Nature Photonics”. The group of Philip Walther of the University of Vienna has also recently demonstrated that robots can learn faster when using quantum resources and borrowing schemes from quantum computation. This new achievement represents one more step towards a future where quantum artificial intelligence become reality.

Original publication: 

Michele Spagnolo, Joshua Morris, Simone Piacentini, Michael Antesberger, Francesco Massa, Francesco Ceccarelli, Andrea Crespi, Roberto Osellame, Philip Walther, et al: “Experimental quantum memristor”. In: Nature Photonics

DOI: 10.1038/s41566-022-00973-5

Related

Source: University of Vienna

Recent Posts

Nanocrystalline Cores for Low‑Loss MHz Chip Inductors

25.3.2026
36
Schematic illustration of the electric double layer of porous carbon electrodes at elevated potentials in a a conventional electrolyte and b a weakly solvating electrolyte; source: authors

Researchers Presented Lignin-based Electrolyte for 4V Supercapacitors with Low Self‑Discharge

19.3.2026
26
Researchers developed a polymer capacitor by combining two cheap, commercially available plastics. The new polymer capacitor makes use of the transparent material — pictured here, with vintage Penn State athletic marks visible through it — to store four times the energy and withstand significantly more heat.  Credit: Penn State

Penn State Demonstrated Polymer Alloy Capacitor Film with 4× Energy Density up to 250C

19.2.2026
78

TU Wien Sets New Benchmark in Superconducting Vacuum Gap nanoCapacitors

16.2.2026
24

Exxelia Publishes Micropen White Papers for Printed Electronics

26.1.2026
39

Researchers Demonstrated 32nm Aluminum Vacuum Gap Capacitor

20.1.2026
55
Credit: Institute of Science Tokyo

Researchers Demonstrated 30nm Ferroelectric Capacitor for Compact Memory

2.1.2026
55

Reliability Improvement in BaTiO3 MLCCs Using Ni–Sn and Ni–In Alloy Electrodes

19.12.2025
168

Researchers Present Novel Graphene-Based Material for Supercapacitors

3.12.2025
62

Upcoming Events

Apr 21
16:00 - 17:00 CEST

Heatsink Solutions: Thermal Management in electronic devices

May 5
16:00 - 17:00 CEST

Understanding and Selecting Capacitors – Fundamentals, Technologies and Latest Trends

May 19
16:00 - 17:00 CEST

Designing Qi2 Wireless Power Systems: Practical Development and EMC Optimization

View Calendar

Popular Posts

  • Buck Converter Design and Calculation

    0 shares
    Share 0 Tweet 0
  • Boost Converter Design and Calculation

    0 shares
    Share 0 Tweet 0
  • Flyback Converter Design and Calculation

    0 shares
    Share 0 Tweet 0
  • LLC Resonant Converter Design and Calculation

    0 shares
    Share 0 Tweet 0
  • Dual Active Bridge (DAB) Topology

    0 shares
    Share 0 Tweet 0
  • MLCC and Ceramic Capacitors

    0 shares
    Share 0 Tweet 0
  • Ripple Current and its Effects on the Performance of Capacitors

    3 shares
    Share 3 Tweet 0
  • MLCC Case Sizes Standards Explained

    0 shares
    Share 0 Tweet 0
  • Capacitor Charging and Discharging

    0 shares
    Share 0 Tweet 0
  • Plastic Materials Dielectric Constant and DF

    4 shares
    Share 4 Tweet 0

Newsletter Subscription

 

Passive Components Blog

© EPCI - Leading Passive Components Educational and Information Site

  • Home
  • Privacy Policy
  • EPCI Membership & Advertisement
  • About

No Result
View All Result
  • Home
  • Knowledge Blog
  • PCNS

© EPCI - Leading Passive Components Educational and Information Site

This website uses cookies. By continuing to use this website you are giving consent to cookies being used. Visit our Privacy and Cookie Policy.
Go to mobile version