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

Wormholes can make the stable quantum channel in quantum computers.

Wormholes could make it possible to create stable quantum entanglement. But the problem is that nobody is made the quantum-size wormhole between quantum-size black and white holes. But virtualization of the wormhole is possible. 

Theoretically, things like wormholes between quantum-size black holes and white holes can make it possible to create the most powerful quantum computers that ever be possible to make. The problem is that aiming the direction of the quantum-size wormhole is difficult and the system requires enormous energy mass. 

The wormhole can aim by using superpositioned and entangled particles. And the creation of the required black hole can make by pressing the electron or proton in the middle of the ball-shaped chamber. That system can be possible somewhere in the future. 

Fullerene nanotubes inside maser-rays could use as virtual wormholes.

Fullerene nanotubes inside maser-rays could use as virtual wormholes. The maser ray protects the nanotube against outcoming radiation. And the quantum entanglement can form through that miniature fullerene channel. 

But the fullerene nanotubes are making it possible to make the virtual wormhole between the objects. The idea of the use of carbon nanotubes is that the quantum computer system basing on the idea that the carbon nanotube protects the qubit or quantum entanglement against the outcoming effects. The quantum entanglement can form through the nanotube. 

If researchers want to make it easy that energy in the virtual wormhole travels to one direction they can put Bose-Einstein condensate at another end of the nanotube. And that causes the effect where energy travels to the side of the Bose-Einstein condensate.

And if the maser ray will travel around that nanotube channel there is the possibility to make the virtual wormhole. In that virtual wormhole would be no crossing electromagnetic fields because maser rays that travel outside the nanotube deny access to natural quantum fields in that tube. That thing protects the qubit against the outcoming effect. 

The quantum computer could use the electrons that travel between laser impulses as the qubits. In that kind of system. Is easy to deny electromagnetic turbulence. The laser ray will aim at the electron. And it will push the electron through the nanotube or virtual wormhole. 

See the next terms: 

Black hole

Quantum entanglement 

White hole 

Wormhole

https://miraclesofthequantumworld.blogspot.com/