Use of Teslaphoresis for Construction of Practical Quantum Computer for Use with Machine Learning Algorithms, And Demonstration of Grover's Algorithm with Neuron Culture


#1

Related to: [Name of Request for Research]

Please Do Not Just Take These Ideas and Run With Them Without Me!
I am attempting to increase my own involvement in Artificial Intelligence research and quantum computation. Please feel free to contact me at trevornestorslab@gmail.com. I would be more than willing to assist in the development or writing of a thesis or experiment (or at least get my name on there!)

I have worked extensively with tesla coils and I came to an epiphany a few weeks ago for the assembly of a practical quantum computer, but could not come to the appropriate materials required to build such a device. I came to a video of a demonstration of teslaphoresis at Rice University, and I think that the way it has been presented, teslaphoresis would be of great benefit on this frontier. I have had a discussion with Dr. David Redish (Neuroscience Prof) at the University of Minnesota and Dr. Babak Ayazifar at the University of California Berkeley (Electronics Prof) as well as discourses with a friend of mine that works at the Google facility in Mountainview, California.

There is a lot going on with regards to quantum computer / machine learning research, and my discourses are many pages long, so it might be difficult for me to condense the information effectively, but it all started when this Google colleague of mine commented on the difference between the arcs produced by vacuum tube tesla coils, which produce straight spearlike arcs, and the arcs produced by tesla coils with more modern switching components (IGBTs and MOSFETs) which seem to produce more jagged and diffuse arcs.

From my own understanding and what I’ve gathered, the human brain relies on quantum computing - specifically it allows all neurons to simultaneously “vote” to come up with a cohesive bound experience under a collective field potential that collapses in the form of impulses (the wavefunction collapse). Neurons are simply tiny RLC circuits and condense or coalesce under the form of an electromagnetic field, much like you have seen in your experiments. This forms receptor densities in the brain. Receptor types are very much similar to different kinds of switching components, and they produce different wavepackets - tonic/phasic signaling types known in neuroscience.

Dopamine receptors are like the vacuum tubes, they produce straight paths without much side branching - they reinforce connections - in the arcs - the dendrites.

I will attach a few photographs and documents and maybe you will be able to put the dots together. Quantum computers are vital because they allow the traversal of large amounts of data in tractable amounts of time and also break most modern cryptography, so there is a race for the production of the world’s first practical quantum computer that does not require kilowatts of power and subzero kelvin temperatures to operate. I believe that I have solved a great deal of the puzzle.

Backpropagation under this model is instantaneous and thus with such a computer, the fast fourier transform could be done, making it a practical quantum computer.

When I was working with my tesla coils I started to notice a strange phenomenon associated with designs employing vacuum tubes as their switching element. If you are unfamiliar with Nikola Tesla’s work, in his original designs there was a mechanical “switching element” - a spark gap, that would dump potential stored in a primary tank capacitor. With modern technology, we can digitally switch the coil - there is no need for such crudity in design. Modern switching elements are MOSFETs and IGBTs, and the plasma filaments look like a typical “lightning bolt” plasma discharge - jagged - diffuse.

you can see the fractal geometry of the arcs as they “branch out.” There is a certain degree of randomness associated with these arcs because of variations in resistance in the air in the room, the distortion of the ionization paths due to entropy (the heat causes them to rise and mutate and diffuse). What is interesting is that quantum fluctuations are actually amplified and manifest in the geometry of these arcs - of these plasma filaments. Now here is the mysterious part - in a tesla coil with a vacuum tube switching element, the arcs look much different - they look like little spears (or maybe lightsabers):

This is a profound difference, and my friend asked me about this. Several hours of discussion ensued. The reason for this is essentially just the difference in wave packets that the switching element produces. There is a direct relationship to this and the way that dopaminergic networks in the brain reinforce behavior.

Neurons are RLC circuits - they are like little tesla coils, and they have a resonance. They have different “switching elements” too - different receptors with varying densities across the brain’s 3 dimensional topology, and the circuits have varying degrees of resistance in a 3 dimensional topological gradient that are dependent upon neural connections. In AI research, these connections and their resistance are described as weights in the “connectivity” between neural nodes:

So what does this have to do with dopamine and tesla coils? The answer is tonic and phasic wavepackets. Dopaminergic receptors are more like “vacuum tubes,” and produce straight reinforced paths and prune out side connections. This was a fascinating realization because it also ties into a theory of cognition that I believe explains how the brain, as compact as it is, is able to come to decisions so quickly that are complex, and there are direct ramifications for the development of real-world practical quantum computers that do not require subzero kelvin temperatures or kilowatts of power to operate.

Parkinson’s disease is the disease of the paralysis of the will, and this model also explains parkinson’s symptomology

The brain is unable to cohere possibilities in a tractable amount of time into an impulse which is required for movement, in a similar way that a quantum computer can traverse a database in O(nlogn) time. There is a paralysis of will because the brain cannot effectively iterate through all of the branches effectively without the right switching type (dopamine receptors)

The arcs produced by the coils amplify quantum fluctuations, there is a degree of “randomness” associated with them, but it is like the Pope said, the universe was not made out of a principle of chaos - this chaos is fundamental to intelligence itself.

Classical models cannot explain this, the intelligence must come from a higher dimensional space - without a true “random number generator” the branches would not be able to effectively explore the possibilities necessary for intelligence in the human mind:



This also explains things like why there seems to be a fundamental randomness to the universe. Einstein said that “God does not play dice with the universe,” however, the universe appears to be random and run by probabilities. That is because the electromagnetic force is a smaller fractal representation of the gravitational force which permeates all 11 dimensions, and so it only approximates

I have been discussing this issue of chaos with one of my friends who is studying physics at the University of Texas at Austin. Chaos is a physics phenomenon that is not well understood, in the fields of science there are many principles that are not really well understood. The principle of turbulence, for example:

Inline image 1

How can something so orderly, like this smoke rising from a candle, become so decoherent and chaotic? What explains it? and how can you make sense of it, what theory brings it all together? The candle smoke falls apart into a mess. Somehow, gravity brings everything back together - gravity operates through 11 dimensional spacetime. Everything will come together, just not in a way that people can understand, like in this example, there is no equation or set of equations that can model turbulence, but I believe in Spinoza’s interpretation of God - a God of order and structure - there is a “plan” that brings everything back together. Our limitations in our own ability for inspection limits us to interpret the world fully. The mind works through electromagnetic fields, which are smaller fractal representations of the gravitational field, and so our ability to understand is fundamentally limited in this way, but if we work together we can approximate an understanding of the bigger picture, and that is why attempts to explain turbulence are only approximations.

Back in the early days of computing, there was the idea of “functional programming.” This approach quickly became inefficient and messy, and programmers became paralyzed by the number of pointers and underlying data, such that a bigger different approach was necessary. This approach became known as “object oriented programming,” and so these “objects” could have global effects across different pieces of code, and their influence could extend beyond their local block. In quantum computers, every idea is a field in the same way that in OOP, every idea is an object - except the difference is that a field has values at every location in spacetime, whereas objects are fundamentally limited in scope by the way silicon computers are produced

Turbulence is also an unsolved physics mystery - I have attached a discourse below discussing this in relation to the bitcoin graph

Turbulence is seen in all sorts of different domains - even religion - because every idea is a field with fractal ramifications, not a discrete data type or even “object.” Object oriented programming attempts to utilize the human brain’s “second memory system” as described by Dr. David Redish more effectively through the principles of inheritance, polymorphism, and scope. Information in this system is stored continuously, not in discrete space limited by the planck length.

http://000024.org/religions_tree/religions_tree_8.html

There is even a simple mathematical proof that shows that a classical computational network would be fundamentally unable to devise of a quantum mechanical computational machine

Neurons are most certainly RLC circuits, like tesla coils


#2

I’m sorry, I was attempting to copy a dialogue I had but it doesn’t look like it translated well via copy and paste


#3

In effect, what I am writing here is that the brain is a quantum computer, there is a method for creating a practical quantum computer that does not require subzero kelvin temperatures and kilowatts of power to operate, neurons in the brain are RLC circuits like tesla coils with switching elements similar to the vacuum tubes / MOSFETS or IGBTs in modern tesla coil designs, these switching elements in the brain model are receptors, dopamine receptors reinforce behavior and circuits in a way that is similar to the way that vacuum tube driven tesla coils produce straight spearlike arcs rather than the more open diffuse arcs produced by other switching element driven coils, that neurons “self-assemble” in a way similar to the way that carbon nanotubes self-assemble in the Rice University teslaphoresis experiments, that the brain is capable of the fast fourier transformation, mirror neurons encode entanglement states, and that is how memories are stored in the brain


#4

Each of the neurons “votes” in that the smaller fields created by each neuron form a greater electromagnetic field that in turn effects all of the parts through resonance (like a large tesla coil broadcasting to a number of smaller coils), receptors and neurons coalesce and form clusters or gradient density across the brain’s three dimensional topology


#5

I really would like to get a small culture of neuron cells from thermo fischer and have them perform grover’s algorithm
to prove they can do the FFT


Hodgkin–Huxley model - Wikipedia
The Hodgkin–Huxley model, or conductance-based model, is a mathematical model that describes how action potentials in neurons are initiated and propagated. It is a set of nonlinear differential equations that approximates the electrical characteristics of excitable cells such as neurons and cardia…

the hodgkin huxley model proves they are rlc circuits like tesla coils
and they have nanotubules that help them guide to destinations
the dendrites
sort of like the carbon nanotubes in the self assembling teslaphoresis experiments
at rice university
i think clusters of neurons form self consistent axiomatic systems and they reflect the outside world through quantum entanglement of mirror neurons, that is how memories are formed
how the outside world is imprinted in the mind of a person through memory
through the principle of entanglement
that way a person even when removed from an experience or system can still intuit how the system might evolve even if separated from it
every idea is a field, much like in oop every idea is an object
it has a scope that extends throughout spacetime, and isn’t limited like in oop languages
a field has a value at every point in spacetime
the advantage of oop is that it is more intuituve because of this very reason - it takes advantage of the programmer’s ability to do FFT
and reflect that in his code
polymorphism, inheritance, scope - all properties of fields
a group of neurons wired together form an electromagnetic field that interacts
as a colective field
i was looking at time lapse videos of neurons and they seem to assemble similarly to the way the teslaphoresis carbon nanotubes self assemble under an electromagnetic field
neurons have nanotubules
what is interesting is while i was thinking about it there was a strange old man in the IMU and he had this bag of spools of string
and he kept talking about a story in greek mythology
where there was a labyrinth and to find the end of the maze, the hero had to go into the maze with a "cloo"
this spool of string
and leave the path behind
to find the way out
cloo is a greek word that refers to this story
aka basis for the word clue
and he also said that words were formed by quantum mechanical means
when i heard this i was like okay whatever man
this old man doesnt know what he is talking about
but i am looking at the way that neurons find their targets and it seems to me that this is largely true


Growth Cone Extension Example
A Xenopus laevis retinal neuron extends in stagnant culture media over the course of 30 min. Images for time-lapse compilation were taken ever minute.
youtube.com
he gave me one of the spools and said he "gave me a cloo"
he didnt know me or any of this stuff i was looking into i didnt even mention it
i dont believe that memories can ever be encoded into a machine created with silicon
it must take advantage of the entanglement effect, otherwise there will be no “mind body” duality as described by descartes
the mind and physical world are orthogonal, and the effects drugs have for example are orthogonal to the information stored in the networks

#6

https://www.sciencedirect.com/science/article/pii/S0960077906003213

http://000024.org/religions_tree/religions_tree_8.html

The Halting problem, Godel’s incompleteness theorem, Heisenberg’s uncertainty principle - these are all proofs that are saying the same thing, the mind - intelligence - quantum computers, must rely on this higher dimensional space to do calculations - the quantum behavior of field potential collapse, coalescence, and in this context, teslaphoresis

There is even a simple mathematical proof that shows that a classical computational network would be fundamentally unable to devise of a quantum mechanical computational machine

Neurons are most certainly RLC circuits, like tesla coils


#7


#8

(Neuronal growth cones are Ariadne’s Thread)
The ability to map concepts across mutliple domains, such as, in this example, to relate two seemingly unrelated things - an ancient Greek myth to the neurobiological evolution of a neural network - illustrates the continuous fractal nature of memory storage in quantum cognitive systems


#9

Thermo Fischer Scientific does provide neuronal cultures of various kinds, I believe with full confidence that a culture of these cells is capable of performing quantum reverse cryptographic hash functions. Mirror neurons encode entaglement states and this is fundamental to the way that memory works in the brain. With a culture of these cells under specific conditions operations could be performed not capable of a standard classical computer, and is capable of threatening modern cryptography.

Clusters of neurons form self consistent axiomatic systems and they reflect the outside world through quantum entanglement of mirror neurons; that is how memories are formed and how the outside world is imprinted in the mind of a person through memory (source memory) - through the principle of entanglement - that way a person even when removed from an experience or system can still intuit how the system might evolve even if separated from it. Learning is performed through personification - relatedness to self. Every idea is a field, much like in OOP every idea is an object - it has a scope of “visibility” that extends throughout spacetime, or in the case of OOP, a program. The developmental advantage to an Object Oriented Programming Language in computer science is fully reliant on the ability of the programmer to perform the Fast Fourier Transformation - to “feel” his way through code.

I believe that the Allais, Ellsberg and Machina paradoxes are evidence themselves of this, and beyond that the Hiesenberg Uncertainty Principle, Godel’s Incompleteness Theorems, and the Halting Problem. The brain relies on a higher dimensional space to perform specific types of calculations that are more efficient - otherwise it would be paralyzed by possibilities with intractable problems, like in the case of Parkinson’s Disease.

A field has a value at every point in spacetime. The advantage of OOP is that it is more intuitive because of this very reason - it takes advantage of the programmer’s ability to do the Fast Fourier Transformation and navigate the code more effectively, and reflect that in his code polymorphism, inheritance, scope - all properties of fields.

A group of neurons wired together form an electromagnetic field that interacts as a collective field that in turn affects all of the individual parts, much like pendulums falling into dynamic synchrony. I was looking at time lapse videos of neurons and they seem to assemble similarly to the way the teslaphoresis carbon nanotubes self assemble under an electromagnetic field.

I don’t believe that “memories” in the sense that they exist in the brain can ever genuinely be encoded into a machine created with silicon - such a machine must take advantage of the entanglement effect, otherwise there will be no “mind body” duality as described by descartes. The mind and physical world are orthogonal, and the effects drugs have for example are orthogonal to the information stored in the networks. This accounts for the “subconscious,” for “feelings” and “epiphanies” not rigorously explained through higher abstraction reasoning such as through the logos.

And yet, the idea that the central nervous system relies on quantum mechanical effects is not so radical, even modern scientists themselves have found that the sense of smell may very well be reliant on the resonance of molecules, and that resonance is translated into the sensation of smell - some smells are sharp, others are more mellow - falling on a spectrum - there are fundamental issues with the key-lock model. Birds of various kinds are found to rely heavily on the Earth’s electromagnetic field for navigation. No, it is quite an obvious possibility that I believe has been severely lacking in research.


#10

In machine learning research, the principle of backpropagation implies a quantum mechanical effect. Even Penrose himself postulated that the brain relied on quantum mechanical effects - backpropagation is the summing over the entire field potential to collapse to an impulse. The fractal geometry of neural networks is similar to those of plasma filaments radiating from a source, and whether a network is reinforced or diffused or in the state of exploration can be largely modulated by the wave packet type - tonic or phasic signaling - analogous to the difference between the wavepackets produced by vacuum tube tesla coils and IGBT/MOSFET tesla coils, respectively. The switching element makes the difference - in the case of neuroscience - the receptor type.


#11

For a quantum mechanical AI machine, it is important to recognize, that the capability of the machine to perform independent self-sustaining behavior correlates with the machine’s likelihood of behavior deviating from the intended will of the programmer. In a source-memory paradigm, such a machine, like a human, learns through relatedness to self. We only learn about the outside world through this sense of personification - for example, when describing the behavior of a rock when thrown into the air, we might say that the rock “goes up” and then “hits a peak” and then “goes back down” in a parabolic arc. This intuition, however, forces the observer to put himself in the place of the object. The rock is not actually making the decision to act behaviorally, but when describing the rock’s behavior, the way it is described is as if it interacts with the outside world in this way (it “goes up” then “goes down”). It is impossible to divorce the self - the observer - from the environment - they are intimately intertwined, as I have described earlier.


#12

The Fast Fourier Transformation is precisely what allows a person to arrive at an epiphany or instantaneous gut feeling decision - a Fast Fourier Transformation allows the instantaneous traversal of all memories - a summing over an entire person’s being and memories - to arrive at a result - in a tractable amount of time.

I do hope that in the course of reading these conclusions any person reading this material will not neglect to include me in any research they are performing, lest I become a poor isolated inventor like Nikola Tesla. With an appropriately designed nanomaterial, teslaphoresis would be a vaible technique for performing nonclassical computation.


#13

https://www.newscientist.com/article/dn18371-brain-entanglement-could-explain-memories/


#14

THE SCIENTIFIC DILEMMA OF THE PRINCIPLE OF TURBULENCE IN CHAOTIC SYSTEMS

Turbulence is the increasingly high sensitivity to initial conditions over time - this high sensitivity amplifies quantum fluctuations and is responsible for the ability to use the Fast Fourier Transformation - a computational procedure that relies on higher dimensional space.

How can something so orderly, like this smoke rising from a candle, become so decoherent and chaotic? What explains it? and how can you make sense of it, what theory brings it all together? The candle smoke falls apart into a mess. Somehow, gravity brings everything back together - gravity operates through 11 dimensional spacetime. Everything will come together, just not in a way that people can understand, like in this example, there is no equation or set of equations that can model turbulence, but I believe in Spinoza’s interpretation of God - a God of order and structure - there is a “plan” that brings everything back together. Our limitations in our own ability for inspection limits us to interpret the world fully. The mind works through electromagnetic fields, which are smaller fractal representations of the gravitational field, and so our ability to understand is fundamentally limited in this way, but if we work together we can approximate an understanding of the bigger picture, and that is why attempts to explain turbulence are only approximations.

Classical models cannot explain this, the intelligence must come from a higher dimensional space - without a true “random number generator” the branches would not be able to effectively explore the possibilities necessary for intelligence in the human mind (the Halting Problem). This also explains things like why there seems to be a fundamental randomness to the universe. Einstein said that “God does not play dice with the universe,” however, the universe appears to be random and run by probabilities. That is because the electromagnetic force is a smaller fractal representation of the gravitational force which permeates all 11 dimensions, and so it only approximates.

The arcs produced by the coils amplify quantum fluctuations, there is a degree of “randomness” associated with them, but it is like the Pope said, the universe was not made out of a principle of chaos - this chaos is fundamental to intelligence itself.


Machine Learning Algorithms Do Not Produce Consciousness - A Warning Against "Trusting" AI Machines (Teslaphoretic Quantum Brain Theory) INTELLIGENCE IS NOT THE SAME AS CONSCIOUSNESS