The physics-based quantum tools will replace current AI networks.

 The physics-based quantum tools will replace current AI networks. 

The future of AI is material that involves mass memories, power sources, and data storage in one entirety. The next big step in neural networks is the material-based AI where carbon-fiber-based material is turned to the computer.  

The problem with regular AI-based neural networks is that the quantum computers that becoming more common will break any code that protects the neural network. And that means the future of networks is in a physics-based self-learning system. That is based on quantum computing. 

The programmable materials will replace current AI networks quite soon. The simplest version of that type of neural network is the microchip, whose kernel involves the AI programs. In that model, the AI is integrated into microchips. And that makes data-handling easier for that computer. That type of structure is not a physics-based material. 

But it tells about the route where the next-generation neural networks are going. The material-based solutions where intelligent material involves mass memory, and also integrated ability to handle data. The next-generation neural network can be the system where quantum entanglement is made into the same material as mass memories and other things. 

The next-generation computer can be a three-layer nanorod. That structure makes it act like the human brain. The quantum computer created by using three-layer nanorods can be as intelligent as the human brain. The quantum entanglement will created horizontally between photons or electrons trapped in the nanorod structure. Vertical entanglements transmit information between those layers. 

But the fact is that that kind of extremely complex nanostructure can have multiple layers of qubits. The atom-size quantum computers can hang in the nanorod. And that thing makes it possible to create new and powerful systems. 

"Learning with light: This is what the dynamics of a light wave employed inside a physical self-learning machine could look like. Crucial are both its irregular shape and that its development is reversed exactly from the time of its greatest extent (red). Credit: Florian Marquardt, MPL" ((https://techxplore.com/news/2023-09-physics-based-self-learning-machines-current-artificial.html)

"Artificial intelligence as a fusion of pinball and abacus: In this thought experiment, the blue positively charged pinball stands for a set of training data. The ball is launched from one side of the plate to the other. Credit: Florian Marquardt, MPL". (https://techxplore.com/news/2023-09-physics-based-self-learning-machines-current-artificial.html)

The mass memories are anchored in the nanorod that also transmits information forward. In that system, the atom-size quantum computers operate as staged waves. When electricity that transports information faces the group of those nano-size quantum computers those systems will process information and send it forward. In nanorods could be millions or even trillions of atom-size nanocomputers. 

There could be thousands of quantum entanglements in that kind of atom-size quantum computer. Electrons can have multiple quantum entanglements between them. Also, things like protons, neutrons, quarks, and gluons can form quantum entanglements. And in a hole in the net-structure of the nanotube can be one or more atom-size quantum computers. That makes those systems more powerful than any existing network. 

This is the reason why quantum materials are so interesting. By using extremely accurate photon and electron impulses the quantum system can drive material or its atoms and other particles so close to each other that they can make quantum entanglement between photons, electrons, or even protons that are in "pockets" of that material. 

The idea is that a photon trap where the single photon is trapped transfers energy into that superpositioned and entangled photon pair. The problem is that this thing requires an ability to control the material in its entirety and that makes the ADNR (Aggregated diamond nanorod) nanotubes interesting. 

In ANDR nanotubes carbon atoms are close to each other. And then that thing makes it possible to make stable "lockers" for photons. In ideal models, the next-generation AI-based structure involves all components like mass memories, power sources, and data handling units along with computer programs forming the entirety. 

The next-generation computer can be a nanotube, where a nano-size structure involves the quantum system. And in some other visions, the electric wire will turn into nano- or quantum-size computers. The system would be different from modern AI-based neural networks. The wire itself works as a computing system. And that thing makes the computer faster and more accurate than ever before. 

https://techxplore.com/news/2023-09-physics-based-self-learning-machines-current-artificial.html

https://en.wikipedia.org/wiki/Aggregated_diamond_nanorod