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Real programmers don't comment their code. It was hard to write, it should be hard to understand.

Hello,

Yet more precision about quantum computers and about artificial intelligence..

I am a white arab, and i think i am smart since i have also
invented many scalable algorithms and algorithms..

Look at the video of IBM about there quantum computer, i think that IBM has worked on an interface with hardware circuits that permits to translate from your favorite programming language to the quantum computer language, so there is no need to learn a quantum programming language, so it will become much more easy to program a quantum computer, so look at the video here to notice it:

https://www.ibm.com/quantum-computing/?p1=Search&p4=43700050386405608&p5=b&gclid=Cj0KCQjwp86EBhD7ARIsAFkgakiGC294e584qtmsskplAXivote6smMgZ5hzM2a9Cd8u4bX8qg6ZFSEaAu-uEALw_wcB&gclsrc=aw.ds

And read my following previous thoughts:

I invite you to read the following interesting article about quantum computers:

https://www.mjc2.com/quantum-computing-logistics-manufacturing-optimization..htm

So as you are noticing that a quantum computer permits to do Quantum Parallelism that is special, since a register of a quantum computer is
not the same as a register of classical computer, the register of a quantum computer can be any combination of numbers, all at the same time using quantum effects of a Quantum Computer. If a register can represent any number from 0-1000, then in a quantum computer the register could be set up so that it is a mixture of all numbers from 0-1000 at the same time, and so quantum computer can also do Quantum Parallelism that is so powerful on for example logistics and on artificial intelligence, but you have for example to rewrite the energy "minimization" in deep learning of artificial intelligence into a quantum computing way that can be done really really fast by Quantum Parallelism , but notice that
a quantum computer can not replace a classical computer, a quantum
computer can only be used for some kinds of problems. And i invite
you to read below my way of doing energy minimization in artificial intelligence with PSO(Particle Swarm Optimization), so
read below since i am explaining more in my thoughts below what is
artificial intelligence, so read them below:

So i invite you to learn more about quantum computers and quantum computing in the following website of IBM and DWAVE:

https://www.dwavesys.com/tutorials/background-reading-series/quantum-computing-primer

And:

https://www.ibm.com/quantum-computing/?p1=Search&p4=43700050386405608&p5=b&gclid=Cj0KCQjwp86EBhD7ARIsAFkgakiGC294e584qtmsskplAXivote6smMgZ5hzM2a9Cd8u4bX8qg6ZFSEaAu-uEALw_wcB&gclsrc=aw.ds

And I invite you to read the following interesting article:

This is your brain on Quantum Computers

https://singularityhub.com/2016/10/02/this-is-your-brain-on-quantum-computers/

And read the following news:

New Passive Quantum Error Correction Could Be The Breakthrough for Large Scale Quantum Computers

Look at the following video:

China claims ‘quantum supremacy’ with new supercomputer | DW News

https://www.youtube.com/watch?v=E5MBAJJU9Hk

More philosophy about what is artificial intelligence and more..

I am a white arab, and i think i am smart since i have also invented many scalable algorithms and algorithms, and when you are smart you will easily understand artificial intelligence, this is why i am finding artificial intelligence easy to learn, i think to be able to understand
artificial intelligence you have to understand reasoning with energy minimization, like with PSO(Particle Swarm Optimization), but
you have to be smart since the Population based algorithm has to guarantee the optimal convergence, and this is why i am learning
you how to do it(read below), i think that GA(genetic algorithm) is
good for teaching it, but GA(genetic algorithm) doesn't guarantee the optimal convergence, and after learning how to do reasoning with energy minimization in artificial intelligence, you have to understand what is transfer learning in artificial intelligence with PathNet or such, this transfer learning permits to train faster and require less labeled data, also PathNET is much more powerful since also it is higher level abstraction in artificial intelligence..

Read about it here:

https://mattturck.com/frontierai/

And read about PathNet here:

https://medium.com/@thoszymkowiak/deepmind-just-published-a-mind-blowing-paper-pathnet-f72b1ed38d46

More about artificial intelligence..

I think one of the most important part in artificial intelligence is reasoning with energy minimization, it is the one that i am working on right now, see the following video to understand more about it:

Yann LeCun: Can Neural Networks Reason?

https://www.youtube.com/watch?v=YAfwNEY826I&t=250s

I think that since i have just understood much more artificial intelligence, i will soon show you my next Open source software project that implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO, and i will write an article that explain
much more artificial intelligence and what is smartness and what is
consciousness and self-awareness..

And in only one day i have just learned "much" more artificial intelligence, i have read the following article about Particle Swarm Optimization and i have understood it:

Artificial Intelligence - Particle Swarm Optimization

https://docs.microsoft.com/en-us/archive/msdn-magazine/2011/august/artificial-intelligence-particle-swarm-optimization

But i have just noticed that the above implementation doesn't guarantee the optimal convergence.

So here is how to guarantee the optimal convergence in PSO:

Clerc and Kennedy in (Trelea 2003) propose a constriction coefficient parameter selection guidelines in order to guarantee the optimal convergence, here is how to do it with PSO:

v(t+1) = k*[(v(t) + (c1 * r1 * (p(t) – x(t)) + (c2 * r2 * (g(t) – x(t))]

x(t+1) = x(t) + v(t+1)

constriction coefficient parameter is:

k = 2/abs(2-phi-sqrt(phi^2-(4*phi)))

k:=2/abs((2-4.1)-(0.640)) = 0.729

phi = c1 + c2

To guarantee the optimal convergence use:

c1 = c2 = 2.05

phi = 4.1 => k equal to 0.729

w=0.7298

Population size = 60;

Also i have noticed that GA(genetic algorithm) doesn't guarantee the optimal convergence, and SA(Simulated annealing) and Hill Climbing are much less powerful since they perform only exploitation.

In general, any metaheuristic should perform two main searching capabilities (Exploration and Exploitation). Population based algorithms ( or many solutions ) such as GA, PSO, ACO, or ABC, performs both Exploration and Exploitation, while Single-Based Algorithm such as SA(Simulated annealing), Hill Climbing, performs the exploitation only.

In this case, more exploitation and less exploration increases the chances for trapping in local optima. Because the algorithm does not have the ability to search in another position far from the current best solution ( which is Exploration).

Simulated annealing starts in one valley and typically ends in the lowest point of the same valley. Whereas swarms start in many different places of the mountain range and are searching for the lowest point in many valleys simultaneously.

And in my next Open source software project i will implement a powerful
Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO.

Thank you,
Amine Moulay Ramdane.

Subject	Author
Yet more precision about quantum computers and about artificial intelligence..	Amine Moulay Ramdane
Re: Yet more precision about quantum computers and about artificial	Chris M. Thomasson
Re: Yet more precision about quantum computers and about artificial	Chris M. Thomasson
Re: Yet more precision about quantum computers and about artificial	Bertram Schuller

Real programmers don't comment their code. It was hard to write, it should be hard to understand.

tech / sci.math / Re: Yet more precision about quantum computers and about artificial intelligence..