►
Description
Esta charla explicaremos los fundamentos de la Computación Cuántica, como la combinación de los principios de la Física de Partículas (física y/o mecánica cuántica) con la Ciencias de la Computación y la Teoría de la Información a dado paso a la creación de esta tecnología que revolucionará el futuro en algunas industrias. Hablaremos de los retos a los que nos enfrentamos para poder producir esta tecnología, y las aplicaciones o problemas específicos en los que impactará la computación cuántica.
A
O
Hello
everyone,,
how
are
you,
Fernanda,?
Let's
talk
a
little
bit
today
about
what
I
found
Milani
using
the
example
of
the
Titanic
data
set,
just
a
little
bit
here
about
who
I
am
I
have
a
degree
in
information
management
from
UFPE
I
am
a
master's
student
in
Computer,
Science,
I'm,
better
FPS,
today,
I'm
as
a
software
engineer
in
the
water
swim,
and
also
just
the
lecturer
and
teacher
in
the
area
of
data
analysis
and
last
and
not
least,
I'm
in
love
with
couscous.
A
A
Anything
else
it
is
a
science
that
studies
the
information,
its
process
of
capturing
the
transformation,
the
generation
and
later
the
data
analysis.
So
let
salts
in
summary
is
for
us
to
get
extra
going
to
value.
Our
data
right
is
transforming
data
into
information
to
improve
decision
making
Hi
and
what
are
the
most
used
programming
languages
in
the
area
of
dedera,
size,
Python
and
r
are
the
only
ones.
No,
we
also
have
some
languages.
A
that
are
growing
like
Júlia
scale
is
today
the
most
used
are
Python
and
r,
ok
And,
then
we
enter
Mach
Lane
and
what
would
be
machine
learning
and
it
is
translated
there.
Machine
learning,
so
Marche,
Milane
or
machine
learning
is
a
subfield
of
Computer
Science
that
evolved
from
the
study
of
pattern,
recognition
and
the
theory
of
computational
learning
and
artificial
intelligence.
So
when
we're
talking
about
Mach
Lane
we're
talking
about
and
trying
to
find
patterns
in
our
data,
so
that
we
can
make
a
prediction
for
the
future.
are
the
companies
that
use
Mach
Lane?
A
We
have
some
examples
here,
like
Netflix.
When
you
comment
on
the
movie,
based
on
the
movies
you
see,
sistiu
is
kneaded
when
we
are
looking
for
a
certain
book,
and
she
also
says
that
those
who
bought
that
book
also
bought
these.
So
people
who
have
a
profile
similar
to
yours,
then
you
can
also
like
what
people
who
have
a
profile
similar
to
yours,
liked
Before.
They
also
indicate
it
says
Spotify
here
when
it
recommends
us
a
song,
A
playlist,
based
on
everything
we've
heard
so
only
Mach,
Lane
algorithms,
that
understand,
right,.
A
They
know
our
pattern
and
our
profile
and
make
recommendations
based
on
that
in
our
profile.
Right
today
we
have
much
more
personalized
recommendation,
let's
say
so:
it's
marcilane
who's
there
running
behind
and
then
what
are
the
main
ones
and
those
of
machine
learning
What
are
the
main
flat
types
we
will
have
the
supervised
or
unsupervised
and
the
reinforcement
learning
and
then
what
would
be
for
each
of
them
in
the
supervised
Let's
say
that
we
will
have
the
data
there
at
7:00,
when
we
already,
there
will
be
a
high
column
in
the
dataset,
right?
What.
A
We
always
predict
so
here.
I
already
brought
the
example
of
the
exact
date
of
the
Titanic,
where
we
have
the
passenger's
ide,
with
some
Survival
of
whether
he
survived
or
not
the
sex,
the
age
and
how
much
he
paid.
I
brought
some
columns
here.
We
don't
have
all
the
forms
here,
but
we
have
some
examples
here.
So
here
we
already
have
the
column.
If
that
passenger
survived
the
Titanic
or
not.
So
we
already
have
our
target
so
supervised
learning
I'm
going
to
separate
my
set
here
into
the
predictive
variables
that
will
be
my
x
So.
A
What
is
the
passenger's?
Gender
What
is
the
age.
How
much
did
he
pay
and
will
our
Y
be
our
target
that
here
o
01,
then
algorithm?
It
will
learn
who
survived
the
Titanic.
The
most
were
women,
were
they
men
or
were
they
in
the
age
group
when
they
paid?
They
were
in
the
first
in
the
second
in
the
third
class,
or
the
group
will
learn
what
was
the
standard
there
of
the
people
who
were
o
survived
and
he's
going
to
tell
us
when
we
pass
on
data
that
he
hasn't
met.
A
Yet
he's
going
to
say:
oh
this
person
would
survive
the
wouldn't
survive.
The
Titanic
we're
the
supervised
learning
I
would
be
the
unsupervised
learning.
We
call
that
now
we
have
a
database
is
of
animals,
dogs
and
cats,.
There
must
be
a
base
there
with
images
of
what
a
dog
and
a
cat
would
be
like,.
We
have
several
types
of
dogs
and
cats
there
and
then
what
is
the
learning
when
we
go
through
this
base,
these
images
for
an
unsupervised
algorithm?
A
What
does
it
do
and
we,
since
in
this
case
we
don't
have
the
target
column,
you
know,
I,
don't
know
who
is
a
dog
and
there's
a
cat,
I,
don't
have
it
in
my
base?
I
just
have
the
images,
the
images
of
dogs
and
images
of
cats
or
when
we
switch
to
an
unsupervised
algorithm.
Until
what
will
happen,
then
I'll
just
leave
quickly.
A
So
what
will
happen?
The
algorithm
it
tends
to
group
the
s
similar,
right,,
so
he'll
take
the
cat
images
there
and
throw
them
in
a
group
and
he'll,
take
the
dog
images
and
throw
them
in
another
group.
So
he'll
learn
what
are
the
characteristics
of
a
cat
and
what
are
the
characteristics
of
a
dog
and
he'll
grouping.
So
the
tendency
is
that
we
have
two
groups,
one
of
cat
and,
where
dog
and
taken
to
sit
without
percent.
No,
so
the
more
data
is
better.
A
The
algorithm
will
learn,
right,
but
the
tendency
is
for
it
to
do
that.
It
creates
two
groups
and
all
the
cats
are
in
a
group
all
of
me
in
another
group.
So
then
we
have
unsupervised.
Learning
is
when
we
have
to
create
groups
right,
I
have
a
group
of
people
I
want
to
open
a
chat
or
similarity.
What
are
the
similar
people
here
then
I
don't
have
my
something
variable,
and
then
we
pass
the
unsupervised
algorithm,
Hi
and
reinforcement.
Learning
that
we
learn
from
interactions
with
the
environment
is
more
often
used
in
robotics.
So.
A
We
also
learn
from
our
own
experience,
for
example,
the
little
robot
that
cleans
the
house,
right,
that
cleans
the
house,.
So
it
learns
to
read
from
its
own
experience,
for
example,.
If
it
hits
a
piece
of
furniture,,
it
learns
that
there
is
a
piece
of
furniture
there.
there.
He
doesn't
need
to
pass
and
come
back
and
go
to
the
empty
places.
So
that's
when
we
call
it
reinforcement,
learning,
right,.
A
A
The
data
is
used
for
our
dataset,
it
will
take,
it
will
create
the
Bootstrap
Data
Set
What.
Is
this
ogun
I
hope
data
CEP
is
a
statistical
removal
technique
that
and
they
involve
random
sampling
of
a
set
of
data
with
replacement,
that
is,.
He
will
take
our
data
and
create
some
samples
there
and
then
a
second
item
there,.
He
will
verify
the
attribute
that
best
separates
the
data
and
I
put
it
like,
for
example,
Gini
index,
and
what
is
this
Gini
index
the
Gini
index?
A
It
Checks
the
impurity
of
our
data,
that
is,
the
higher
the
Gini
index
is,
the
greater
the
homogeneity
of
our
data
and
the
smaller
it
is,
the
more
dispersed
our
data
are,
then
Rondon
strong.
He
goes
do
this
attribute
check
and
better
separate.
The
data
is
to
create
and
from
our
tree,
right,
we
saw
it
sent
Force
would
be
random
trees,
so
it
will
cut
in
third
place.
It
will
create.
The
classes
are
and
will
create
these
trees
and,
according
to
the
sampling
there
that
he
did
there,
the
bus
does
not
pass,.
A
A
A
These
trees.,
in
the
end,
it
will
bring
the
average
of
the
performance
of
all
these
trees
and
then
I
brought
a
tree
here,
for
example,
So.
How
does
a
tree
work
in
this
example?
Here
we
have.
Let's
say
we
wanted
to
know
if
we
are
going
to
give
credit
to
a
person
or
not
it's
a
credit
analysis
here.
So
how
does
it
happen?
He
put
here
in
ours
there
just
the
age,
so
the
best
attribute
for
him
that
he
considered
there
by
the
demands
ni.
A
It
was
the
age
so
the
age
better
to
separate
our
data
here.
So
then
he
takes
the
age,
puts
it
here
at
the
root,
and
now
he
will
have
to
stay
there.
The
person
is
young.
If
so,
he
will
go
down
another
take
here
in
our
tree,
so
he
will
ask.
Is
it
young
and
the
student
or
not
Yes?
We
will
release
the
credit
for
the
person.
If
it
is
young
and
the
student,
we
will
not
release
the
credit
for
that
person
that
one
will
get
the
adult.
A
What
is
the
age
is
the
adult,
so
if
it
is
an
adult
it
already
releases
directly,
building
is
not
an
adult
yes
and
takes
a
credit,
and
here
will
be.
The
gentleman
is
serious
there.
He
will
go
down
another
layer,
credit
analysis
of
that
person
that
is
password
is
bad
or
very
good.
So
if
it
is
bad,
No
I
will
not
give
credit,
and
if
is
very
good
like
this,
we
are
going
to
release
the
credit
for
this
person
password.
A
So
here
we
have
an
example
of
how
a
decision
tree
works,
so
Random
Force
I
used
Random
Forest.
It
will
have
100
trees
of
this
and
in
the
end
we
will
have
the
average
right
from
curasse
is,
for
example,
of
all
the
trees.
So
that's
why
a
downforce
that
it
tends
to
be
better
than
just
the
decision
tree
that
just
a
tree
Hi
and
then
we
go
to
the
code,
right.
Usually
Live
Gold
passed
them,
but
people
only
have
20
minutes
there.
A
He
lecture
I'm,
going
to
show
the
code
here
and
we
're
going
to
comment
and
I'm
going
to
explain
so
First
step
is
to
import
the
libraries
we're
going
to
use
so
that
I
used
nativity
waves
is
for
the
manipulation
and
treatment
of
data,
not
father
numerical
processing,
doctors,
free-se
borne
for
visualization
in
the
creation
of
graphs
and
the
Sexy
Lane
library
that
the
Library,
where
we
can
create
models
of
Mach
Lane.
So
there
are
several
algorithms
available
in
the
and
claim
so
here,
I
used
it
lizei.
A
Is
the
train
of
this
Split
to
separate
our
data
into
test
and
training,
right
training
test
and
the
37
or
randomforestclassifier
In
this
case
we
have
a
classification
right,
I
want
to
classify
who
survived
or
not
what
the
difference
between
classification
and
regression
regression
is
when
the
learn.
The
number
continue.
For
example,
I
want
to
I
have
historical
data
from
the
stock
exchange
there,
for
example,
I
want
to
know
what
the
value
of
the
stock
market
is
tomorrow
or
in
a
week,
so
continuous
data
right.
A
So
we
are
going
to
do
a
regression
in
this
case.
Here
a
we
have
a
binary,
classification,
right,,
it's
10
or
you
survived
or
you
didn't
survive.
So
we
go
in
there.
Classification
and
here,
I
take
it,
I
connect,
Hugo's
training,
data
and
I
haven't
said
it
yet,
but
this
data
set
is
available,.
He
doesn't
want
to
go
back
and
it's
a
platform
of
competition
in
the
area
of
having
a.
If
so,
there
are
several
competitions
there.
A
This
is
one
of
the
ones
with
people
available,
so
you
have
access
I
set
Titanic,
you
can
train
and
also
create
your
algorithm.
You
tested
that
the
algorithm
not
only
a
downforce
and
check
which
one
did
better
so
it's
available
over
at
Carol,
and
here
look
what's
in
your
base
here.
I
am
The.
First
five
lines:
So
we
have,
as
I,
showed
you
in
the
example
of
the
supervised,,
the
Passenger
Haiti
column,
and
it
is
the
passenger
Airbag,.
It
is
made
of
steel.
A
Class
1,
2
or
3,
the
name
of
the
passenger,
the
gender
was
female
or
male.
The
age
is
this:
sibsp
would
be
the
number
of
siblings
or
spouses
on
board
the
Titanic.
The
fish
is
the
number
of
parents
or
children
around
Titanic,
the
tiger
of
only
the
number
of
the
passenger's
ticket.
This
beast
is
the
passenger's
tariff.
So
how
much
did
he
pay
for
the
cabin
that
would
be
the
cabin
number
and
boarding
says
it's
the
port
of
embarkation
So?
These
are
the
columns
that
we
have
available
here.
A
A
It
tells
us
the
type
of
data
of
each
column,
then
like
this
Garden
as
integer
Survival
of
as
integer
there,
not
even
me
here
as
an
object,
would
be
equivalent
to
string
in
Python,
so
it
forms
for
us,
the
data
type
of
each
column,
and
here
I
start
to
do
an
exploratory
analysis.
So.
Let's
analyze
here,
What
was
the
percentage
of
women
who
survived
the
Titanic?.
A
There
were,
right,,
so
they
are
women's,
is
divided
by
the
Total
length.
It
returns
in
total
for
us
here,
o
a
survival
rate
of
74
in
relation
to
women,
so
only
to
always
remember
that
what
I
remember
does
the
law
it
returns.
314
is
a
total
of
women
AND
the
sum
of
a
column,
01
So.
It
will
add
up
everyone
who
doesn't,
right,,
so
we
have
233
women
who
survived
that,,
then
the
74
percent
the
same
thing
for
the
male
gender.
A
So
we
there,
we
had
I
already
left
here
in
this
Barra
and
we
have
577
men
aboard
the
Titanic
in
this
Data
Set
and
109
survived.
So
there
we
have
a
rate
of
18%
of
men
who
survived
the
Titanic
so
that
I
planted
an
arm
right
here.
I
also
put
an
example:
how
we
can
use
this
method
here
from
the
panda
to
the
old
man
and
such
and
it
does
exactly
the
same
thing
in
it.
A
Tell
us
there,
according
to
the
sex
EA
column,
that
I
went
through,
so
we
had
587
men
and
314
women,
and
here
I
planted
this
information
using
the
cyborg
ICMS
here,
and
here
we
can
see,.
We
had
a
lot
more
men
than
women,
with
a
much
higher
survival
rate
for
women
and
pa
for
men,
because
we
know
that
women
were
the
priority
right
in
the
pits,
so
your
women's
Keyboards
will
survive,
while
only
eighteen
percent
of
men
survived-
and
here
I
put
this
one
by
Survival
of
right
who
survived
or
not.
A
So
if
we
take
the
men
here,
the
total
men
and
here
how
many
will
survive
While.
Here
we
have
the
total
number
of
women
and
the
majority
survived.
74
percent
here
is
to
protect
the
histogram
in
relation
to
cities,
so
we
can
see
how
the
age
distribution
was,.
We
see
that
It's
well
distributed
here
between
the
highest
submission.
Actually
between
20
and
40
years
old,
here,
18
19
years
old.
Here,
up
to
40
years
old,
we
had,
we
will
have
age
here:
zero,
zero.
A
A
There
too,
and
the
people
who
did
not
survive
were
in
class
3,
right,,
because
I
I
believe
that
class
is
my
priority,,
and
here
I
planted
this
map,.
This
crazy
map,,
a
correlation
between
the
variables
So.
If
we
look
at
what
we
can
take
from
here,,
we
have
a
negative
correlation
between
the
class
and
the
value
the
fare
here
for
the
passenger
and
the
father
felt,,
the
higher
the
fare,,
the
lower
the
class,
right,.
So
the
higher
I
paid,
I
was
there
in
class
one,.
A
We
are
not
patient,,
it's
the
people
who
paid
more
for
the
fare
here,.
There
is
a
negative
correlation
between
these
variables
and
here
I
am
doing
what
I
am
transforming
the
gender
column
into
numbers.
What
I
will
want
to
use
sex
in
the
model?
Ok,
I
believe
that
the
gender
variable
is
important
to
determine
whether
or
not
the
person
survives
the
Titanic,
so
I
I
did
where
it
is
masculine,.
It
was
zero.
One
of
this
girl
was
one
we
can
see
here.
A
The
zero
is
this:
is
a
boy
is
one
so
I
transformed
this
categorical
variable
into
numeric
to
optimize
it
in
our
Mach
Lane
algorithms,
because
Antão
Force
cannot
understand
variables
categorical
there
for
me
need
to
make
this
transformation
if
we
pass
the
sex
column
there
with
him.
The
f
we
will
receive
the
error,
and
here
I
am
separating
my
training.
So
my
training
is
in
the
the
truth.
Is
that
I
'm
deleting
the
columns
that
have
zero
age,?
We
have
some
options,
I
could
replace
the
average
age
with
one
for
the
mode,
I.
A
Don't
know,
but
I
preferred
to
highlight
those
lines
where
we
don't
have
the
age
value,
because
I
I'm
also
going
to
use
this
community
in
my
model,
yeah,,
the
model
can't
understand
null
values,
they're
here,
look,,
we're
being
triggered,.
This
avoids
this
video
here
for
our
model,.
So
in
training,
we
have
everyone
except
the
passenger's
Edi,,
because
it's
just
an
identifier,
so
it
could
be
more
confusing
Honda
model
more
that
helps
so
just
go
there,
right.
That
is
our
target,
so
it
won't
be
here
in
x.
It
will
be
in
y.
A
The
name
too
I
didn't
find
it
interesting
the
day
that
I
only
have
the
identification
from
the
Ticket
Which
was
the
port
of
embarkation
I
took
Which
was
the
cabin
So.
We
will
stay
in
the
training
here
in
the
Play
variables
and
the
class,
the
sex,
the
age
if
there
were
siblings
or
spouses
and
parents
or
children
on
board,
and
how
much
that
he
paid
So.
This
is
our
x
and
our
Y
is
the
result.
A
X,
which
is
my
y
and
I,
left
here,
thirty
percent
of
the
data
for
testing,
so
seventy
percent
of
us
will
train
and
thirty
percent.
We
will
borrow
because
we
need
to
see
how
good
this
algorithm
comes
out
or
data
that
he
hasn't
seen
yet
so
checking
here
we
have
499
lines
to
train
and
215
lines
to
test,
and
now
we
are
going
to
instantiate
our
peacemaker
object,
our
Random
Force,
so
here
I
am
calling
Random,
Force
pasiphae
and
right
after
that,
we
are
going
to
train
so
to
have
to
training.
A
The
other
thing
we
need
to
do
is
give
a
Fit.
Then
Fit
do
X
training
and
2
single
and
we
trained
our
model.
So
here
are
some
parameters
that
you
can
use
that
do
not
give
strength
and
can
be
used,
so
the
documentation
is
very
complete.
Ok,
so
I
want,
as
I
said,
power
was.
It
will
create
without
trees,
but
we
can
be
tar
there
how
many
classes
I
want
to
be
created.
So
it's
in
this
email
is
the
number
of
ar
trees
were
created,
the
bootstrapper.
A
If
we
are
going
to
consider
Bootstrap
or
not
the
right
date
from
there
for
the
law
to
update
to
do
the
random
sampling
right
from
our
database,
we
can
pass
the
total
number
of
fitness
that
the
trees
were
created.
What
is
the
measure,
right
I
said
in
Sydney,
but
there
are
others
other
measures
that
we
can
use
for
and
nothing
in
the
division.
Generally
I
use
it
really
requires,
but
there
are
others,
the
Spiritist
who
can
strategy
used
to
divide
the
node,
the
maximum
depth
of
the
tree.
A
So
these
are
the
parameters
that
we
can
use
when
we
are
training
our
model,
and
here
we
are
going
to
visualize
the
result,.
How
did
this
model
come
out
so
here?
The
result
I
made
a
prediction
of
our
x
test
is
the
our
validation
set,
and
here
it
returns.
The
result
if
Because
they
were
one,
but
here
it
is
difficult
for
us
to
understand
if
they
did
well
or
not.
So
here,
look
I'm
getting
doctors
to
leave.
Call
too,
and
here's
plotting
here,
I'm
showing
one
here
Gustavo.
A
S
in
general,,
the
student
knows
the
accuracy,
right,,
so
our
cure
And.
It
was
78
percent
There's
a
way
to
improve
this
model.
There
are
some
techniques
that
we
can
apply
to
try
to
improve
this
performance.
And.
Then,
how
come
you
can't
check
spturis
the
most
important
variables
for
this
model?
Then
we
have
another
video
and
ports
is
when
we
have
it
in
the
model.
We
are
available
difficult,
I,
can't
where
it
brings
the
importance
of
each
variable
So.
A
Sex
and
how
much
he
paid
here
is
explaining
our
model,
and
here
I
took
only
one
tree
is
the
first
one
to
exemplify
for
you
as
well
as
I,
showed
the
exe
there
mple,
so
here,
I'm
selecting
the
first
tree
of
our
model
of
the
handle
here.
The
Random
LS
is
our
model,
sent
drummer.
This
way,
I'm
opening
one
Here,
it's
zero,
because
the
indexing
in
the
parent
then
starts
at
10,
right,
so
I'm
taking
the
first
tree.
This
code
here
is
just
for
us
to
plot
it
graphically,
right
visually.
A
A
Four
five
event
is
where
this
tree
is
going
as
well
as
we
can
also
prune
this
tree
to
say
there
in
the
algorithm
that
we
just
want
it
to
go
until
it
is
to
unite
And,
then
in
depth.
There
are
others
taken
here,
but
if
you
look
here,
look
at
this
here
it
stops
here.
Then
it
is
observed
here
the
sex
there.
If
it
is
false,
it
goes
down
there
if
he
paid
less
than
or
equal
to
48
thousand.
Here
he
comes
here.
If
he
paid
less
=
39,
he
comes
here
And.
A
Then
he
arrives
at
the
incidence
of
zer
o
and
he
said
that
the
class
is
zero
So.
This
is
how
he
does
this
one,
that
he
creates
the
trees
according
to
these
rules
from
what
he
learned
there
in
our
data
and
he
will
generate
the
trees
here.
I
just
took
a
test
to
show
you
there
I
already
trained
I
already
tested.
A
How
do
I
do
it
to
make
a
prediction
of
the
data
that
arrived
now
So
some
data
arrived
and
I
want
to
know
if
this
person
would
survive
or
not
so
here,
I
did
a
simulation.
Then
I
created
an
Array
with
my
father,
the
data
as
if
they
were
the
data,
for
example.
So
up
here,
I
put
the
columns
that
we
used
to
create
a
model
for
you
to
understand
here.
Look
and
Siri
is
talking
here,
sorry,
guys
it
's.
A
There
ok
And
here,
I
put
the
Let's
say
if
I
I
was
on
board,
the
Titanic
I
was
in
class
2,
my
gender
would
be
a
female
range.
At
my
age,
28
I
had
no
spouse
halfway.
Children
on
board,
then
100
and
I
paid
this
amount
here
for
my
Bullet
So
here
it
is,
I
believe
I
created
a
King
here
with
these
values
and
now
I'm
going
to
go
through
the
predict
of
my
Model
And.