The neural network is the mind of the tiger.

  The neural network is the mind of the tiger. 

The difference between traditional computers and neural networks is the role of the CPU. In traditional computers, the CPU (Central Processing Unit) makes all work, and that causes limits in computing. In neural networks, the CPU preprocesses data that senses send into it. That means the CPU will just recognize the situation and then resend it to a sub-processing unit. The receiving sub-system must have responsibility for that kind of case. That means the subsystem has databases where there are reactions for certain types of events. 

In neural networks, all sub-networks use sorted data. And they are meant to respond to things that senses are sending to the neural network. Each of the subnetworks has certain operational or response areas. And the CPU is like some kind of router. The system finds certain details from the data. Then it can route the information to the right subnetwork. In real life, neural networks could be multiple subnetworks. They can act as AI-based event handlers. 

The complete neural network requires that all sub-networks have also a physical system that runs them. In that case, the system has multiple workstations that run the AI-based systems. In that case, the system will decrease the use of the top CPU. If the system uses multiple independently operating workstations. At can handle multiple problems at the same time. Error detection happens when the neural network uses two or more independently operating data handling units at the same time. 

In an ideal situation, the CPU of the neural network is working as a router that can send information to independently operating subsystems. The independent subsystem has a physical computer and its CPU. In that case, the common CPU of those multiple networked workstations sends a message to a certain subunit, and then the AI-based subunit will make the mission and the system sends the data back to the CPU. 

The shape of the neural network is free. The neural network can be a drone swarm. Or it can be the lots of workstations in some warehouse. The neural networks can collect information from multiple sources. And the neural system's power is that it shares data between multiple computers. The neural network doesn't necessarily learn anything. But it can collect data from very large areas. 

The learning neural network follows some parameters for making its duty. The thing that can be the parameter is the drone that operated the longest time in a certain area. The system can store information about the routes and other things that drones use. Then it can multiply the longest-standing drone's operational profile to another drone. 

And of course system must have multiple parameters like when drones were seen, and where the first impact came. The system interconnects the data from all members of the drone swarm. And then it tries to create an ideal flight path to the target. Here we must remember that the same drone that used to deliver pizza can deliver at least hand grenades. The neural network can interconnect multiple systems like surveillance cameras, satellites, and other things like drone swarms. 

The first programmable quantum computer is made by using neutral atoms.

The ability to use neutral atoms in quantum computers is a remarkable thing. Until now, quantum computers used trapped ions or superconductors in their structure. But the problem with those versions is that they are very sensitive against outside effects. If trapped ion touches the core of the chamber. Or the temperature of a superconductor is rising too high. That thing causes the problems. When we think about the possibility to make the room-temperature operating quantum computer. 

The researchers must "simply" calculate the resistance of the wire is possible to calculate precise points where the qubit reaches a certain state while it loses its energy. By using that information. The computers can calculate the points where qubits are releasing their information. So what if we want to make so-called quantum brains? What would we need for that? There is the possibility that the hybrid system is made by using a nanotechnological structure. The system's core would be made of silicon-carbon material where are chambers. 

Those chambers are connected as an entirety by using nano-tubes. In each chamber is the ion. That anneals by using radio waves or some other electromagnetic radiation.  The brightness of the ion is determining the state of the qubit. The annealing system will measure the brightness of the qubit by using the photovoltaic cells. In that system, the silicon core is also acting as the independent quantum computer. Which controls the quantum annealing system. 

Light is a good data transporter. If the brightness of the laser rays can adjust. That thing can use to transport data in quantum computers. The laser ray can shoot to silicon atoms. And that reaction can turn to electricity that can act as a qubit. The silicon atom-based quantum computers can get their data by using laser rays. And maybe those systems can operate at room temperature. 

The biological quantum computer is one futuristic vision of this system. 

The use of the biological components will decrease the need for energy in the quantum annealing system. 

If we are thinking about the most futuristic way to make the quantum annealing quantum computer. There is the possibility to use the biological components in this system. If we want to put the cells that are creating light in those chambers we could make the light. 

And then the brightness of the light can adjust by using the iris. So the core of that chamber would be equipped with systems that are looking like a camera shutter. And that shutter adjusts the brightness of the light. 

The problem with quantum annealing or other quantum systems is how to make the data travel in lines. The power of the quantum computer is this.  The system can share data with multiple central processing units. The idea is similar to the book that some school classes should read. There are two ways to make this thing. All members of the class are reading the entire book.

Or the teacher can share the book with all members of the team. And then every member of the team is reading small parts of the book. So the book is shared in pieces with team members. That means every person in the team will read only 10 pages from 200 pages of text. 

And after that, the members of the team will tell what happened during their 10 pages. That model is very good if the data mass that the system handles is linear. So the row of the data is like the book. And it will share between central processing units which are operating with a small piece of that data mass. 

When the system shares data there might be some top processor. That processor is like the teacher in the classroom. It preprocesses the data as the teacher looks at the number of pages in the book. And then the teacher asks, which part of the book the members of the team are taking. When the team members are ready. They send the mark that they are done their job. 

There is the possibility. That in the middle of the system is the light source. That shares data to the entirety. In that data flow is the marks where the system cuts it. Then every single part of the system tells others which part it takes to handle. 

It eliminates the work that has no meaning. The error handling requires that somewhere in the system that makes the similar data-handling processes. If those results are the same the solution is right. 

Then the rest of the others would select from the remaining pieces of data. That thing requires complicated structures. Human brains are the biological quantum computer. But to make a copy of human brains engineers must have at least 200 billion data handling units. And controlling those units is a very complicated mission. That requires multi-level quantum computers. And complicated AI. 

https://futurism.com/the-first-reprogrammable-quantum-computer-has-been-created

https://www.sciencealert.com/silicon-quantum-computing-has-reached-over-99-percent-accuracy

https://thoughtsaboutsuperpositions.blogspot.com/