Studying the

1
Studying the brain realization and its
simulated quantum implementation for the Cynthia
robot
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Contents

• Introduction to Cynthia robot.
• The goal of the research.
• Examples of different explanations of the brain
  system
• The research plan.
• Overview on the previous work.
• Simulation steps
• Current work (Project)
• Future work.

3
Introduction to Cynthia Robot
4
Introduction to Cynthia Robot
5
The goal of the research

• To build a block which will act as a brain on
  top of the MNS and NNS.
• This block will control the behavior of the
  robot such that it reflects the learning process
  of the robot as well as the physical phenomena
  that might happened and affect the robot behavior
  similar to the real brain.

- That requires us to study the biological brain
systems
6
Examples of different explanations of the brain
system

• Gerald Edelman Neural Darwinism
• The Theory of
  Neural Group Selection.

7
Neural Darwinism The Theory of
Neural Group Selection.
8
Examples of different explanations of the brain
system

• Stapp The Brain as a Quantum Measuring
  Device

The neural wave function enfolds superposed
possibilities, and then consciousness chooses one
classical branch and annihilates the others. The
choice is "unruly," Stapp (1993, p.32) says.

• Some Physical Phenomena couldn't be represented
  by any low or mathimatical equations

• Twins spiritual link

It is believed that this could be explained using
one of the quantum mechanics features, called ,
Entanglement
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Examples of different explanations of the brain
system

• Yasue Quantum Brain Dynamics and
  Consciousness An Introduction (Advances in
  Consciousness Research, V. 3
• Water Mega molecule

• Ben Goertzel Evolutionary Quantum Computation
  
• Its Role
  in the Brain, Its Realization in Electronic
  Hardware, and Its Implications for the Pan
  psychic, Theory of Consciousness
• Populations of neuronal maps have a quantum
  aspect as well as a classical. The brain is an
  evolving population of quantum neural networks

10
Examples of different explanations of the brain
system

• Set up an ensemble of quantum computers, and
  allow them to evolve.
• Create criteria for judging QC's, and then, in
  the manner of natural selection, allow successful
  QC's to survive and (probabilistically) mutate
  and combine to form new candidate QC's, whereas
  unsuccessful QC's perish.
• The result is that one has quantum computers
  fulfilling desired functions via unknown means.

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Quantum System Theory
E24 (0 1 0 0)

• Hilbert Space

E14 (1 0 0 0)
X
E34 (0 0 1 0)
E1 E2 E3 E4 are orthonormal vectors and
called the bases of the Hilbert space
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Quantum System Theory

• Hilbert Space

13
Quantum System Theory

• Quantum system

• Quantum systems are described by a wave
  function, r , that exists in a Hilbert space.
• The Hilbert space has a set of states, Ei ,
  which is called the set of bases, and the
  system is described by a quantum state, r , which
  is said to be in a linear superposition of the
  basis states Ei , and in general, the
  coefficients are complex numbers.

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Quantum Theory
15
Quantum Theory
0
0
Block Sphere
1
1
Classical BIT
QuBIT
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Quantum Theory

• Measures the qubit state

The quantum system is said to be collapsed when
we make the projection on one of the basis.
That is also called decoherence or the measures.
For example, if we take the projection of
on the 0gt basis then it will be
. is the
probability of the qubit to collapse on the
state 0gt.
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Why quantum

• The increasing speed of the computations as well
  as reducing the size of the computers will lead
  to the quantum mechanics theory will replace the
  classical logic theory.
• Implementation of models of the physical
  phenomena that could not be implemented before.
• Reduction in time and increase in memory
  capacity.
• Parallelism

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Quam using Grover algorithm
Storing Pattern algorithm
Pattern recall algorithm
For a set of m binary patterns with length n,
2n1 qubits are required
Research algorithm the speed of researching is
O( ).
SPEED OF WHAT???
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Storing Pattern algorithm
A quantum algorithm for constructing a coherent
superposition of states (bases), that
corresponds to the patterns, with the amplitudes
of the states in the superposition all being
equal. fgt X1 X2Xn gt Where X1,X2,.,Xn are
n qubits to represent the n bits for every
binary pattern of the m patterns. For example
f gt 10110100gt 11000011gt ..
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Storing Pattern algorithm

• To construct this wave function we need to
  use n1 qubits to be used in the process of
  generation the function.
• fgt X1 X2Xn, G1G2..Gn-1, C1C2gt
• G1, G2, Gn-1 as well as C1 C2 are control
  registers.

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Storing Pattern algorithm

• Three transformation are used in the process of
  generating the function
• S state generation

explain
Where s is the values of the F(z) and s 1,-1
and 1 lt P ltm.
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Storing Pattern algorithm

• Control flip transformation

Lets consider two qubits
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Storing Pattern algorithm

• Control flip transformation

Lets consider two qubits
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Storing Pattern algorithm

• AND transformation

Lets consider three qubits
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Storing Pattern Algorithm
Explain all symbols
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Storing Pattern Algorithm
Step by Step example
To understand the algorithm, lets assume the
following set of learning patterns   D f(01)
-1, f(10) 1, f(11) -1 From D we
can deduce the following Z3 is
01 Z2 is 10 Z1
is 11 n 2 number
of qubit to represent the patterns
m3 number of patterns to be
represented  
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Storing Pattern Algorithm
Step by Step example
1- f gt 00,0,00 gt X10
X20 g10 , C10 and C2 0 2 2-    do the for
loop P3 Z3 01
Z310 Z321 Z4 00
Z410 Z420 Z32 not equal
Z42 flip X2

X2
C2
X2
Then f gt 01,0,00 gt
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Storing Pattern Algorithm
Step by Step example
3- Flip C1 state
C1
C2
C1
Then f gt 01,0,10 gt
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Storing Pattern Algorithm

• 4-  Generate a new state by applying S on C2
  C1

C1
C2
then f gt -1/ 01,0,11 gt
01,0,10gt
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Storing Pattern Algorithm

• 5-   Flip g1 to mark the register 

g1
X1
X2
g1
then f gt -1/ 01,1,11 gt
01,1,10gt 
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Storing Pattern Algorithm

• 26-   Flip C1 which is controlled by g1
•  

g1
C1
C1
then f gt -1/ 01,1,01 gt
01,1,00gt
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Storing Pattern Algorithm

• 5-   Flip g1 again to the normal state  
•  

g1
X1
X2
g1
then f gt -1/ 01,0,01 gt
01,0,00gt 
Saved
Go to step 1Again
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Storing Pattern Algorithm

• The whole process repeated again with start
• f gt -1/ 01,0,01 gt
  01,0,00gt
• after the 3rd loop
• fgt
•  
• that is what is called storing the pattern.

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5 qubit Quam Network Implemantation
?F0
X1
A0
X2
A0
?F0
g1
F1
F0
C1
S
C2
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7 qubit Quam Network Implemantation
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Pattern recall Algorithm

• The idea of pattern recall is collapsing the
  function f gt on the required basis (pattern).

• Grover used his quantum search in data base
  algorithm in
• recalling the pattern. The idea of this
  search is to change the phase of the desired
  state and then rotate the entire f gt around the
  average. This process repeated (3.14/4)
  Where N is the total possible state.
•  

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Pattern recall Algorithm

• The algorithm steps

 1-change the phase of the desired state. 2-
compute the average A 3- rotate the entire
quantum set around the average. fgt
2A-f gt 4- repeat 1-3 for (3.14/4) 5- Measure
the desired state.  
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Pattern recall Algorithm

• Step By Step example
•  

Lets continue on the same example, used in the
learning phase. Lets assume we want to recall
the pattern 01. Since we have only 2 qubits then
the possible combination is 4. fgt will
collapse on the desired state after repeat the
algorithm for (3.14/4)2, which roughly 1
times.  
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Pattern recall Algorithm

• Step By Step example

1- f gt 2  2- f gt 1/
(0,-1,1,1) 3- 3-   Average 1/4 4- 4-   f gt
1/2 (1,3,-1,-1) 5- 5-   Measure the
desire state f gt ( /2) 01gt   
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Pattern recall Algorithm

• It is obvious that the probability of the system
  to collapse on the desired state is ¾ 75.
• The system collapse in the O( ).
• Which means it is faster than the classical NN
  which takes O(N)

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Comparison between the Quam and the NN Hope field
Associative memory.
Quam Hopfield
Max memory capacity .15n
Number of neurons 2n1 n
Pattern recall speed O( ) O(N)
Phase learning speed O(mn) O(mn)
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Comparison between the Quam and the NN Hope field
Associative memory.
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Phase One
The research plan

• Insert a quantum circuit in the command
  execution data path, in the MNS in figure.1. The
  quantum circuit will alter the command slightly.

• Phase Two

• Phase Three

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Phase One
Robot (CRL parcel translator)
MNS ( command initiation)
Quantum Circuit ( Command
alteration)
Servos (Motion)
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Phase Two

• Study designing the quantum circuit such that
  it reflects the learning process of the robot
  brain and matches the behavior, mode and the
  emotion of the robot.

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Phase Two
Quantum Circuit ( Design the
matched Quantum circuit to the required behavior
)
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Phase Three
Robot Brain

• Generalize the Idea by built in a complete
  block on top of the MNS , which will act as a
  brain to the robot.

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Overview on the previous work

• A quantum circuit was introduced using the QUASI
  quantum simulator.
• The theatre robots communicate using CRL (Common
  Robotic Language).
• The inputs for the Quantum circuit will be the
  data between the command tags in the CRL file.
• The present version supports only the following
  command tags for the recognition of inputs to the
  Quantum Circuit.
• They are wait, flush, move, normal, smile, frown,
  cry, look, speak, speed, accel, open and close.

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Simulation steps
Robot (CRL parcel Command translator)
Choose Quantum circuit using Quasi Simulator
Save the circuit into XML data File
Save input data into XML File
Load the XML files Using Quasi Simulator
Generate the Output sequence from the circuit and
save in a file to be used as an alter command to
the servo
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Current work (project)

• Integrate the software which was done in the
  previous work. (current)

Robot (CRL parcel Command translator)
Choose Quantum circuit using Quasi Simulator
Generate the Output sequence from the circuit and
as an alter command to the servo
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Future work

• Design the quantum circuit according to the
  learning process of the brain.
• Implement the brain block in the Cynthia Robot.

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END
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Introduction to Cynthia Robot
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Simulation steps

• Generate a circuit
• A new Quantum Circuit can be built
  using the Quasi simulator. Run the
  Quark2.Quark command inside the Quasi folder.
  (The main program in the Quark class). Two
  windows will open up. In the left window, click
  on CircuitgtNew tab and create a new circuit. Save
  the circuit (Let us assume you saved it as
  MyCircuit.xml).
• 2. Load the input sequence to the circuit
• To load the input sequence, run the
  following command in the folder where the program
  is saved. Ø java CrlAnalysis MyCrlFile.crl
  MyCircuit.xml This will calculate the input
  sequence from the MyCrlFile.crl and will load it
  into MyCircuit.xml

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Simulation steps
3. Simulate the circuit In the left
window of the Quasi program, click circuitgtload
and load the MyCircuit.xml file. Then click run
to finish button. This will simulate the
circuit and the outputs are displayed in the
second window. The data is stored in the xml
format into the file called QuantumOutput.xml
file. 4. Generate new CRL file Now,
the QuantumOutput.xml file contains the results
of the circuit along with their probabilities
alpha2 and beta2. To generate the new CRL file,
we need to run the following command. Ø java
ReadWriteCrl MyCrlFile.crl QuantumOutput.xml
The new CRL file with the name TestOutput.crl
file is created. This file can be
used on the robots and the behavior can be
observed.
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Defining the Quantum ComputerYou don't have to
go back too far to find the origins of quantum
computing. While computers have been around for
the majority of the 20th century, quantum
computing was first theorized just 20 years ago,
by a physicist at the Argonne National
Laboratory. Paul Benioff is credited with first
applying quantum theory to computers in 1981.
Benioff theorized about creating a quantum Turing
machine. Most digital computers, like the one you
are using to read this article, are based on the
Turing Theory. The Turing machine, developed by
Alan Turing in the 1930s, consists of tape of
unlimited length that is divided into little
squares. Each square can either hold a symbol (1
or 0) or be left blank. A read-write device reads
these symbols and blanks, which gives the machine
its instructions to perform a certain program.
Does this sound familiar? Well, in a quantum
Turing machine, the difference is that the tape
exists in a quantum state, as does the read-write
head. This means that the symbols on the tape can
be either 0 or 1 or a superposition of 0 and 1.
While a normal Turing machine can only perform
one calculation at a time, a quantum Turing
machine can perform many calculations at once.
Today's computers, like a Turing machine, work
by manipulating bits that exist in one of two
states a 0 or a 1. Quantum computers aren't
limited to two states they encode information as
quantum bits, or qubits. A qubit can be a 1 or a
0, or it can exist in a superposition that is
simultaneously both 1 and 0 or somewhere in
between. Qubits represent atoms that are working
together to act as computer memory and a
processor. Because a quantum computer can contain
these multiple states simultaneously, it has the
potential to be millions of times more powerful
than today's most powerful supercomputers. This
superposition of qubits is what gives quantum
computers their inherent parallelism. According
to physicist David Deutsch, this parallelism
allows a quantum computer to work on a million
computations at once, while your desktop PC works
on one. A 30-qubit quantum computer would equal
the processing power of a conventional computer
that could run at 10 teraflops (trillions of
floating-point operations per second). Today's
typical desktop computers run at speeds measured
in gigaflops (billions of floating-point
operations per second). Quantum computers also
utilize another aspect of quantum mechanics known
as entanglement. One problem with the idea of
quantum computers is that if you try to look at
the subatomic particles, you could bump them, and
thereby change their value. But in quantum
physics, if you apply an outside force to two
atoms, it can cause them to become entangled, and
the second atom can take on the properties of the
first atom. So if left alone, an atom will spin
in all directions but the instant it is
disturbed it chooses one spin, or one value and
at the same time, the second entangled atom will
choose an opposite spin, or value. This allows
scientists to know the value of the qubits
without actually looking at them, which would
collapse them back into 1's or 0's.
57
Gerald Edelman's Work Topobiology An
Introduction to Molecular EmbryologyNeural
Darwinism The Theory of Neuronal Group
SelectionThe Remembered Present A Biological
Theory of ConsciousnessBright Air, Brilliant
Fire On the Matter of the Mind
58

• Once one has committed oneself to looking at
  groups, the next step is to ask how these groups
  are organized. A map, in Edelman's terminology,
  is a connected set of groups with the property
  that when one of the inter-group connections in
  the map is active, others will often tend to be
  active as well. Maps are not fixed over the life
  of an organism. They may be formed and destroyed
  in a very simple way the connection between two
  neuronal groups may be "strengthened" by
  increasing the weights of the neurons connecting
  the one group with the other, and "weakened" by
  decreasing the weights of the neurons connecting
  the two groups.
• Formally, we may consider the set of neural
  groups as the vertices of a graph, and draw an
  edge between two vertices whenever a significant
  proportion of the neurons of the two
  corresponding groups directly interact. Then a
  map is a connected subgraph of this graph, and
  the maps A and B are connected if there is an
  edge between some element of A and some element
  of B. (If for "map" one reads "program," and for
  "neural group" one reads "subroutine," then we
  have a process dependency graph as drawn in
  theoretical computer science.)
• This is the set-up, the context in which
  Edelman's theory works. The meat of the theory is
  the following hypothesis the large-scale
  dynamics of the brain is dominated by the natural
  selection of maps. Those maps which are active
  when good results are obtained are strengthened,
  those maps which are active when bad results are
  obtained are weakened. And maps are continually
  mutated by the natural chaos of neural dynamics,
  thus providing new fodder for the selection
  process. By use of computer simulations, Edelman
  and his colleage Reeke have shown that formal
  neural networks obeying this rule can carry out
  fairly complicated acts of perception.
• This thumbnail sketch, it must be emphasized,
  does not do justice to Edelman's ideas. In Neural
  Darwinism Edelman presents neuronal group
  selection as a collection of precise biological
  hypotheses, and presents evidence in favor of a
  number of these hypotheses.
• However, I consider that the basic concept of
  neuronal group selection is largelyindependent of
  the biological particularities in terms of which
  Edelman has phrased it. As argued in (Goertzel,
  1993), I suspect that the mutation and selection
  of "transformations" or "maps" is a necessary
  component of the dynamics of any intelligent
  system.
• Edelman's theory provides half of the argument
  that the brain is an EQC it provides evidence
  that the brain is an evolving system. Edelman
  uses nonlinear differential equations on
  finite-dimensional spaces to model the dynamics
  of neuronal groups he does not consider these
  groups as quantum systems. There is much
  evidence, however, that the brain is not as
  "classical" a system as Edelman and other more
  conventional neural net theorists would have it.

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Will we ever have the amount of computing power
we need, or want? If, as Moore's Law states, the
number of transistors on a microprocessor
continues to double every 18 months, the year
2020 or 2030 will find the circuits on a
microprocessor measured on an atomic scale. And
the logical next step will be to create quantum
computers, which will harness the power of atoms
and molecules to perform memory and processing
tasks. Quantum computers have the potential to
perform certain calculations billions of times
faster than any silicon-based computer How
Quantum Computers Will Work by Kevin Bonsor
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I find Jibu and Yasue's perspective quite
appealing. Rather than throwing out all we have
learned about neural networks, in this view, we
must merely accept that there are parallel
quantum systems, working together with neural
networks to create thought. In terms of Edelman's
theory, we need not reject the idea of Neural
Darwinism -- we must merely accept that these
populations of neuronal maps have a quantum
aspect as well as a classical aspect. In other
words, the brain is an evolving population of
quantum neural networks, selected and mutated
based on their functionality in regard to their
interaction with perceptual and motor systems, as
determined by needs of the organism. Edelman,
plus Jibu and Yasue, equals the brain as an EQC.
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But what is EQC all about? The idea is a very,
very simple one. Instead of programming a quantum
computer, set up an ensemble of quantum
computers, and allow them to evolve. Create
criteria for judging QC's, and then, in the
manner of natural selection, allow successful
QC's to survive and (probabilistically) mutate
and combine to form new candidate QC's, whereas
unsuccessful QC's perish. The result is that one
has quantum computers fulfilling desired
functions via unknown means.
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The neural wave function enfolds superposed
possibilities, and then consciousness chooses one
classical branch and annihilates the others. The
choice is "unruly," Stapp (1993, p.32) says, "not
individually controlled by any known law of
physics." So the heart of consciousness is random
on Stapp's view. He hopes that some future
physics will find a law (1993, p.216), but it
certainly looks like barring an enormous
revolution in quantum physics, Stapp has
installed chance deep in his theoretical
framework, where the quantum choices associated
with conscious events take place
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Yasue's Quantum Brain Dynamics 3.1 The brain is
remarkable in that it provides a variety of
substrates for quantum fields. Different brain
substrates for quantum fields have different
functions. The sensory quantum field, for
example, supervenes on oscillating biomolecules
of high dipole moment in the neuronal membrane.
When the pumping rate reaches a critical value,
Froehlich condensation occurs with macroscopic
coherence of quanta (Froehlich, 1968). 3.2
Another quantum field-supporting biosubstrate is
a dense nanolevel web of protein molecules which
penetrates neuronal and neuroglial membrane
boundaries. I call this filamentous web the
"nanolevel neuropil." Inside the neuron the
nanolevel neuropil consists not only of
microtubules but also neurofibrils and other
structures which connect via protein strands to
proteins floating in the cell membrane. Outside
the neuron in the synaptic cleft is the
extracellular matrix of collagen and
glyco-conjugates, which are also connected to
membrane proteins, so that a pervasive web is
formed. 3.3 There are quasi-crystalline water
molecules within the microtubules and associated
with hydrophylic regions on the web of protein
fila- ments. This ordered water is yet another
brain biosubstrate for a quantum field which
supports super-radiance and self-induced trans-
parency within the microtubules (Jibu et al,
1994). 3.4 Jibu and Yasue (1992, 1993) have
proposed, following some earlier suggestions by
Umezawa (e.g. Ricciardi Umezawa, 1967), that
vacuum states of this water rotational field
record memory. I have suggested that the function
of the nanolevel neuropil is cognitive (Globus,
1995). 3.5 There is a fourth quantum field
substrate where an interaction takes place
between the sensory quantum field and the
cognition/memory quantum field. This is a plasma
of charged particles interacting with the
electromagnetic field. The structure of this
bio-plasma is peculiar it is divided into two
very thin layers separated by a permeable
membrane. Membrane channels open and close, and
ions rush back and forth between the two layers
down electrical and chemical gradients. It is in
this perimembranous bioplasma, whose state is
given by the ionic density distribution, that
sensory and cognition/memory quantum fields
interact. In this interaction of quantum fields,
classical orders may be formed (as when the
multiplication of complex conjugates gives a real
number).
64
An evolutionary quantum computer (EQC) is a
physical system that maintains an internal
ensemble of macroscopic "quantum subsystems"
manifesting significant quantum indeterminacy,
with the property that the of quantum subsystems
is continually changing in such a way as to
optimize some measure of the emergent patterns
between the system and its environment. It seems
probable that the brain is an EQC, and that
electronic EQC dissimilar to the brain can also
be constructed a speculative design in this
regard is described, called QELA (Quantum
Evolving Logic Array), involving Superconducting
Quantum Interference Devices interfacing with
re-configurable Field Programmable Logic Arrays.
EQC has interesting implications for a quantum
pan psychic view of consciousness it provides an
explanation of why, if everything is conscious to
some extent, the human brain is so much more
conscious than most other systems. The
explanation is that, via EQC, the brain is able
to maintain significant quantum randomness ("raw
awareness") in a way that is correlated with its
structure and behavior. Only EQC provides this
kind of correlation, because only EQC allows
uncollapsed quantum systems to interact
significantly with the wave-function-collapsed,
classical everyday world. In many- worlds-terms,
EQC allows systems with a broad span over the
range of possible universes to interact
significantly with systems existing in narrow
regions of universe-space.
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4. U/Y v. H/S 4.1 The conception of the brain is
far richer in U/Y than H/S for U/Y, the brain
generates second order quantum fields. A Geiger
counter or Schroedinger's cat box has a quantum
field description (as a Bogoliubov transformation
of the quantized field) but such ordinary
measurement devices do not sustain quantum fields
like the brain does. So reality is described by
wave functions, both microscopic and macroscopic,
and among those macroscopic realities are well-
developed human brains which themselves sustain
quantum fields and their interactions. 4.2 We
should not think of these second order quantum
fields as making measurements but as offering
possibilities to the match. Both sensory input
and cognition/memory participate in the evolution
of the state variable by offering possibilities
to the match, but the latter is far richer than
the former. I have previously called this rich
quantum plenum of superposed possibilities the
"holoworld" (Globus, 1987) and suggested that the
probabilities of the various possibilities are
tuned (Globus, 1995). The more limited
possibilities of sensory input continually
interact with the tuned holoworld, and a
classical order continually unfolds in the
perimembranous bioplasma. 4.3 So instead of a
measurement collapsing the wave function of a
quantum field to a classical order, we have a
match between quantum cognition/memory and
quantum reality, a match in which classical order
is unfolded.
66
Surprisingly enough, one can argue that this is a
viable model of brain dynamics. Edelman, with his
theory of neuronal group selection, has already
made a strong case for the brain as an
evolutionary system. And Jibu and Yasue have made
a good case for the brain as a macroscopic
quantum system. Putting these two together, we
obtain a strikingly solid case for the brain as
an EQC. The EQC explanation of why the human
brain is so acutely conscious then fits right in.

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In order to see that is the inversion about
average, consider what happens when acts on an
arbitrary vector . Expressing D as , it follows
that . By the discussion above, each component
of the vector is A where A is the average of all
components of the vector . Therefore the ith
component of the vector is given by which can be
written as which is precisely the inversion about
averag