Quantum And Neuromorphic Approaches (Neural Computations) Both Promise To Fundamentally Rewrite The Computational Method. 

Quantum Computers, These two approaches have been integrated over the past few years after creating the “quantum memristor” that could form the basis of quantum neural networks.

For people who work in advanced computing, The neuromorphic concept is remarkable structure VLSI structures(System Integrated or chips very large construction ofUses neural.

On the other hand, quantum computers may be weeks not pass, and new news about record-breaking and charm.

A quantum computing feature that uses the elements of quantum computers to achieve exponential computational speeds in specific problems. Another option is to reconfigure our computer chips to replicate how our brains work more effectively. As mentioned, scientists do this by performing neuromorphic calculations say.

The two approaches seek to improve very different aspects of conventional computing. Although the amplification strategy between the two is unclear, it may change after developing the first neuromorphic component that can process quantum information.

^{Comparison of the capacitor, resistor, and inductor performance with memristors in Ohm’s law}

In this regard, the proposed solution is to use memristors. Memristor piece new one in electronics that combines memory and resistance. These components change their electrical resistance based on the amount of current passed through them and store memory from their previous state.

This feature has attracted the attention of neuromorphic engineers because their function is similar to the behavior of biological synapses (connections between neurons in the brain) that change the strength of their relationships depending on the number of times they are stimulated.

Recently, several studies have attempted to use memristors to build computers that more closely resemble the human brain, which could yield exciting results in the long run.

In this regard, physicists at the University of Vienna have taken the idea one step further by developing a component that exhibits similar behavior when processing quantum information.

The scientists’ first research results in an article in the journal Nature Photonics published.

This article focuses on a new piece called the “quantum memristor” that uses integrated photonics technology. This piece spins the photons around a silicon chip to process the information.

While photonic chips typically only perform classical calculations, the researchers designed a chip that could manipulate and modify the quantum states of passing photons.

To achieve this goal, the researchers used the quantum principle of overlap, which May” A quantum system can appear in a combination of more than one state simultaneously,” he says.

They do this by providing the photon with two paths and moving it down at the same time.

This base forms a qubit that can be used to encrypt information. Just as a bit can be 0 or 1, a photon can be in the first or second channel or intersect the two, thanks to the strange properties of quantum mechanics.

However, the main innovation of these researchers is to use this system with an additional circuit that counts the number of photons that pass through one of the paths and uses it to adjust the signal strength in the other path.

The result is the idea of ​​a device that can both process quantum information and display memory behavior.

After training (a method in computer modeling and simulation), the researchers showed that a system consisting of only three quantum memristors, after training (a technique in computer modeling and simulation) on only 1,000 images, teaches the classification of handwritten digits with 95% accuracy.

The same model is simulated with classical computational schemes, which show even using data much more.

The team also showed that a network of their devices could perform quantum tasks beyond any classical instrument. However, it should note that researchers at the University of Vienna have trained their devices to detect the entanglement of quantum systems with 98% accuracy.

They understand how to use a device that combines two different computational paradigms. But quantum neural networks could be a powerful new tool in the post-Moore’s computational period.

As you know, the plan is similar. Of course, it can be (not so close) more effective patterns are formed, and maybe one day, memristors, kernels, central processors neural.