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s
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k
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u
m
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d
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m
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tri
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re
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teristic (ROC) cu
rv
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d
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su
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o
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q
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tu
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-
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m
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th
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K
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d
s
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B
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ain
d
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n
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in
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Dee
p
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Hy
b
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class
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co
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etwo
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Me
d
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Neu
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Qu
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CC B
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SA
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.
C
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r
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s
p
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A
uth
o
r
:
B
ilal R.
Al
tam
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Mo
s
u
l U
n
iv
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s
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Pre
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,
Un
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Mo
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I
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iq
1.
I
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D
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O
N
Ar
tific
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in
tellig
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(
AI
)
h
a
s
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o
m
e
a
p
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f
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l
to
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in
im
ag
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d
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s
s
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ab
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au
to
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ex
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p
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f
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d
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ata
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AI
-
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an
d
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g
in
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r
i
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g
a
p
p
licatio
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s
[
1
]
.
I
n
1
9
8
2
,
Fey
n
m
an
[
2
]
p
r
o
p
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s
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a
b
asic
q
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tu
m
c
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m
p
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tin
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th
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p
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to
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I
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1
9
9
4
,
Sh
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[
3
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co
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a
q
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tim
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[
4
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lead
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to
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ex
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n
tial
s
p
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v
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class
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[
5
]
.
C
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n
tr
ar
y
to
class
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co
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n
ter
p
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ts
,
q
u
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p
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f
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r
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Evaluation Warning : The document was created with Spire.PDF for Python.
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(
B
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3343
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Su
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[
6
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.
On
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f
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d
am
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n
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tio
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v
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c
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th
e
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th
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class
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T
h
is
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as
p
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d
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p
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s
in
p
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m
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ar
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[
7
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,
[
8
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.
T
h
e
ap
p
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s
o
f
im
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if
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ar
e
f
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m
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in
tellig
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Hig
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co
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co
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n
ee
d
[
5
]
,
[
8
]
.
Fo
r
tr
ad
itio
n
al
im
ag
e
class
if
icatio
n
,
f
ea
tu
r
e
e
x
tr
ac
tio
n
a
n
d
class
if
icatio
n
ar
e
m
an
u
ally
d
esig
n
e
d
an
d
th
e
p
er
f
o
r
m
an
ce
d
ec
r
ea
s
es
with
th
e
r
is
e
o
f
d
im
en
s
io
n
ality
an
d
co
m
p
lex
ity
o
f
th
e
d
ata.
Qu
an
tu
m
m
ac
h
in
e
lear
n
in
g
(
QM
L
)
ad
d
r
ess
es
th
is
b
y
m
ap
p
in
g
im
ag
e
d
ata
in
to
q
u
an
t
u
m
s
tates,
lev
er
ag
in
g
q
u
an
tu
m
p
ar
all
elis
m
an
d
in
ter
f
er
en
ce
to
im
p
r
o
v
e
class
if
icatio
n
s
p
ee
d
an
d
q
u
ality
.
Ho
wev
er
,
q
u
an
tu
m
h
ar
d
war
e
r
em
ain
s
in
a
n
ea
r
ly
s
tag
e
.
T
h
is
m
ak
es
alg
o
r
ith
m
s
s
u
s
ce
p
tib
le
to
er
r
o
r
s
an
d
n
o
is
e
[
9
]
.
2.
RE
L
AT
E
D
WO
RK
2
.
1
.
P
re
v
io
us
s
t
ud
ies
So
m
e
b
r
ief
ly
i
n
tr
o
d
u
ce
th
e
co
n
s
id
er
ed
s
u
p
er
v
is
ed
QM
L
s
ettin
g
,
as
well
as
r
ec
all
th
e
b
asic
in
g
r
ed
ien
ts
o
f
a
q
u
an
tu
m
-
cla
s
s
ical
co
n
v
o
lu
tio
n
al
n
e
u
r
al
n
etwo
r
k
(
QC
NN)
.
C
h
en
et
a
l.
[
1
0
]
s
u
g
g
est
a
n
im
ag
e
class
if
ier
th
at
co
m
b
in
e
s
QC
N
N
with
m
u
ltis
ca
le
en
t
an
g
lem
en
t
r
en
o
r
m
aliza
tio
n
a
n
s
atz
(
ME
R
A
)
an
d
f
r
ac
tal
f
ea
tu
r
e
ex
tr
ac
tio
n
.
T
h
eir
m
eth
o
d
,
wh
ich
is
b
ased
o
n
th
e
id
ea
s
o
f
a
ten
s
o
r
-
n
et
wo
r
k
,
d
o
wn
s
ca
les
im
ag
es
an
d
r
u
n
s
q
u
an
tu
m
p
r
o
ce
s
s
in
g
,
an
d
is
m
o
r
e
ac
cu
r
ate
th
an
a
class
ical
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
o
n
b
r
ea
s
ts
ca
n
ce
r
d
a
tasets
,
d
em
o
n
s
tr
ates
th
e
b
en
ef
its
o
f
q
u
an
t
u
m
f
ea
tu
r
es
an
d
th
e
b
e
n
ef
its
o
f
h
y
b
r
id
QC
NN
im
ag
e
r
ec
o
g
n
itio
n
.
Haf
ee
z
et
a
l.
[
1
1
]
p
r
e
s
en
t
a
h
y
b
r
id
co
m
p
ac
t
m
o
d
e
l
th
at
co
m
b
in
es
a
two
-
q
u
b
it
v
ar
iatio
n
al
q
u
an
tu
m
cir
cu
it
with
a
cla
s
s
ical
C
N
N
f
o
r
b
in
ar
y
class
if
icatio
n
task
s
.
T
h
eir
m
o
d
el
is
d
esig
n
ed
f
o
r
n
o
is
y
i
n
ter
m
ed
i
ate
-
s
ca
le
q
u
an
tu
m
(
NI
SQ)
-
er
a
d
ev
ices
an
d
v
alid
ated
o
n
s
m
all
im
ag
e
d
atasets
.
T
h
e
r
esu
lts
in
d
icate
a
h
ig
h
class
if
icatio
n
ac
cu
r
ac
y
o
f
9
0
.
1
%,
alo
n
g
with
d
im
in
is
h
ed
o
v
er
f
itti
n
g
an
d
en
h
an
ce
d
g
en
er
aliza
tio
n
.
Sen
o
k
o
s
o
v
et
a
l.
[
1
2
]
i
n
tr
o
d
u
ce
two
h
y
b
r
id
q
u
an
tu
m
n
eu
r
al
n
etwo
r
k
m
o
d
els
to
s
o
lv
e
c
o
m
p
u
tatio
n
al
ch
allen
g
es
in
im
ag
e
class
if
icatio
n
.
T
h
e
f
ir
s
t
ap
p
r
o
ac
h
u
s
es
p
ar
allel
p
ar
am
eter
ized
q
u
an
tu
m
cir
cu
its
(
PQC
s
)
b
eh
in
d
class
ical
co
n
v
o
lu
tio
n
al
lay
er
s
,
o
b
tain
i
n
g
ac
c
u
r
ac
y
eq
u
al
to
9
9
.
2
1
%
f
o
r
m
o
d
if
ie
d
N
atio
n
al
I
n
s
titu
te
o
f
Stan
d
ar
d
s
an
d
T
ec
h
n
o
lo
g
y
(
MN
I
ST)
an
d
o
v
er
9
9
%
f
o
r
m
ed
ical
MN
I
ST.
T
h
e
s
ec
o
n
d
m
o
d
el
d
em
o
n
s
tr
ate
d
h
o
w
a
q
u
an
v
o
lu
tio
n
al
lay
er
ca
n
b
e
u
s
ed
to
d
o
wn
s
am
p
le
an
i
m
ag
e
wh
ile
s
till
ac
h
iev
in
g
co
m
p
etitiv
e
ac
cu
r
ac
y
with
f
ewe
r
p
ar
am
eter
s
.
T
h
is
s
h
o
ws
th
e
ef
f
icien
cy
an
d
g
en
e
r
aliza
tio
n
ab
ilit
y
o
f
th
e
q
u
an
tu
m
-
en
h
an
ce
d
f
o
r
m
s
in
ab
u
n
d
an
t d
ata
s
ets.
A
h
y
b
r
id
m
o
d
el
is
p
r
esen
ted
in
[
1
3
]
,
m
ed
ical
q
u
an
tu
m
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
MQ
C
NN)
,
th
at
in
teg
r
ates
QC
NN
with
a
p
r
e
-
tr
ain
ed
R
esNet
-
5
0
ar
ch
itectu
r
e.
T
h
e
ap
p
r
o
ac
h
en
h
an
ce
s
b
io
m
e
d
ical
im
ag
e
class
if
icatio
n
o
f
m
ed
ical
MN
I
ST
b
ased
d
ataset.
Ach
ie
v
in
g
an
ac
cu
r
ac
y
o
f
9
9
.
6
%,
9
9
.
7
% p
r
ec
is
io
n
,
an
d
9
9
.
7
%
F1
-
s
co
r
e,
MQ
C
NN
s
y
s
tem
o
u
tp
er
f
o
r
m
s
b
o
th
th
e
s
tan
d
-
alo
n
e
QC
NN
an
d
R
esNet
-
5
0
m
o
d
els.
T
h
e
r
esu
lts
co
n
f
ir
m
th
e
u
s
e
o
f
q
u
an
tu
m
an
d
class
ical
m
eth
o
d
s
to
g
et
h
er
to
en
h
a
n
c
e
th
e
class
if
icatio
n
p
er
f
o
r
m
an
ce
.
B
u
t
th
e
m
eth
o
d
[
1
3
]
in
tr
o
d
u
ce
th
e
s
ca
lab
le
q
u
an
tu
m
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
e
two
r
k
(
SQC
NN)
t
o
cr
ea
te
a
q
u
an
tu
m
n
eu
r
al
n
et
wo
r
k
th
at
is
s
ca
lab
le
to
o
v
er
co
m
e
h
ar
d
war
e
lim
itatio
n
s
o
f
cu
r
r
e
n
t
q
u
an
t
u
m
s
y
s
tem
s
.
T
h
e
m
o
d
el,
wh
ich
is
im
p
lem
en
ted
u
s
in
g
T
en
s
o
r
Flo
w
q
u
an
tu
m
,
e
m
p
lo
y
s
v
ar
io
u
s
d
ev
ices
to
ex
tr
ac
t
d
ata
f
ea
tu
r
es
in
p
ar
allel.
I
t
class
if
ies
MN
I
ST
an
d
f
ash
io
n
MN
I
ST
with
9
9
.
7
9
%
ac
cu
r
ac
y
,
wh
ich
is
s
u
p
er
io
r
to
th
e
c
u
r
r
en
t
q
u
an
tu
m
n
eu
r
al
n
etwo
r
k
s
an
d
s
h
o
ws
g
o
o
d
s
ca
lab
ilit
y
an
d
ac
cu
r
ac
y
f
o
r
im
a
g
e
class
if
icatio
n
task
s
.
2
.
2
.
Q
ua
ntu
m
ma
chine le
a
rning
Ma
ch
in
e
lear
n
in
g
is
tr
an
s
f
o
r
m
in
g
s
ev
er
al
f
ield
s
s
u
c
h
as
m
ed
ical
d
iag
n
o
s
is
,
f
in
a
n
cial
m
o
d
elin
g
,
an
d
au
to
n
o
m
o
u
s
s
y
s
tem
s
.
C
lass
ic
al
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
f
ac
e
ch
allen
g
es
with
lar
g
e,
h
ig
h
-
d
im
en
s
io
n
al,
an
d
co
m
p
le
x
d
atasets
b
ec
au
s
e
o
f
co
m
p
u
tatio
n
al
b
o
ttlen
ec
k
s
an
d
m
em
o
r
y
co
n
s
tr
ain
ts
.
M
a
ch
in
e
lear
n
in
g
a
n
d
lear
n
in
g
th
eo
r
y
,
as
class
ical
th
eo
r
ies,
ca
n
b
e
r
ef
o
r
m
u
lated
wi
th
in
th
e
q
u
a
n
tu
m
m
ec
h
a
n
ical
f
r
am
ewo
r
k
[
1
4
]
.
I
n
m
ath
em
atica
l la
n
g
u
ag
e
,
th
is
e
m
b
ed
d
in
g
g
iv
es r
is
e
to
th
e
s
o
-
ca
lled
QM
L
[
1
5
]
.
Sin
ce
th
e
in
v
en
tio
n
o
f
QM
L
,
th
e
f
u
ll
ex
ten
t
o
f
QM
L
ap
p
licatio
n
s
h
as
n
o
t
y
et
b
ee
n
d
i
s
co
v
er
ed
.
Ho
wev
er
,
it
ca
n
b
e
s
p
ec
u
lated
th
at
all
th
e
r
eg
io
n
s
d
ep
icted
i
n
Fig
u
r
e
1
will
b
e
im
p
ac
ted
b
y
QM
L
.
C
h
em
is
tr
y
,
m
ater
ials
s
cien
ce
,
s
en
s
in
g
an
d
m
etr
o
lo
g
y
,
class
ical
d
ata
a
n
aly
s
is
,
q
u
an
tu
m
er
r
o
r
co
r
r
ec
tio
n
,
an
d
q
u
an
tu
m
alg
o
r
ith
m
d
esig
n
ar
e
s
o
m
e
in
s
tan
ce
s
th
at
ar
e
lik
ely
to
b
e
p
o
s
itiv
ely
in
f
lu
en
ce
d
b
y
QM
L
[
1
6
]
.
So
m
e
o
f
th
ese
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
26
:
3
3
4
2
-
3
3
5
1
3344
ap
p
licatio
n
s
h
a
v
e
d
ata
th
at
is
in
tr
in
s
ically
q
u
a
n
tu
m
m
ec
h
an
i
ca
l,
an
d
th
u
s
it
is
n
atu
r
al
to
u
s
e
QM
L
(
as o
p
p
o
s
ed
to
class
ical
m
ac
h
in
e
lear
n
in
g
)
f
o
r
th
em
[
1
7
]
.
Fig
u
r
e
1
.
Key
a
p
p
licatio
n
s
f
o
r
QM
L
[
1
7
]
2
.
3
.
Q
ua
ntu
m
-
cla
s
s
ica
l c
o
nv
o
lutio
na
l neura
l net
wo
rk
I
n
r
ec
en
t
y
ea
r
s
,
n
ew
o
p
p
o
r
tu
n
ities
h
av
e
b
ee
n
p
r
o
p
o
s
ed
to
i
m
p
r
o
v
e
t
h
e
f
ea
s
ib
ilit
y
an
d
ex
p
r
ess
iv
en
ess
o
f
class
ic
d
ee
p
lear
n
in
g
m
o
d
els
th
an
k
s
to
QM
L
.
QC
NN
s
wer
e
p
r
o
p
o
s
ed
as
a
h
y
b
r
id
q
u
an
tu
m
-
class
ical
f
r
am
ewo
r
k
th
at
c
o
m
b
in
es
p
r
o
p
er
ties
o
f
q
u
an
t
u
m
c
o
m
p
u
tin
g
with
C
NN
m
o
d
els
[
1
8
]
.
B
y
u
tili
zin
g
q
u
an
tu
m
p
ar
allelis
m
en
tan
g
lem
en
t
an
d
s
u
p
er
p
o
s
itio
n
,
QC
NNs
ca
n
p
e
r
f
o
r
m
th
e
class
if
icatio
n
o
f
co
m
p
lex
d
atasets
m
o
r
e
ef
f
icien
tly
th
a
n
th
ei
r
class
ical
co
u
n
te
r
p
ar
t
with
in
th
o
s
e
d
o
m
ain
s
wh
ich
ar
e
o
f
h
i
g
h
d
im
en
s
io
n
ality
a
n
d
co
m
p
u
tatio
n
ally
ex
p
e
n
s
iv
e,
s
u
ch
as m
ed
ical
im
ag
in
g
,
f
in
a
n
c
e,
an
d
c
y
b
er
s
ec
u
r
ity
[
1
9
]
.
QC
NN
s
f
o
llo
w
th
e
s
tr
u
ctu
r
e
o
f
class
ical
C
N
Ns
b
u
t
lev
er
ag
e
m
eth
o
d
s
f
r
o
m
q
u
an
tu
m
co
m
p
u
tin
g
to
en
h
an
ce
t
h
eir
c
o
m
p
ac
tn
ess
a
n
d
th
e
q
u
ality
o
f
r
ep
r
esen
tatio
n
o
f
f
ea
tu
r
es.
QC
NNs
o
p
er
ate
o
n
a
s
im
ilar
p
r
in
cip
le
as
class
ical
C
NN
s
,
o
n
ly
th
e
y
im
p
lem
e
n
t
(
s
o
m
e
o
r
all)
o
f
th
e
class
ical
co
n
v
o
lu
tio
n
al
lay
er
s
u
s
in
g
q
u
an
tu
m
g
ates
an
d
q
u
an
tu
m
cir
cu
its
th
at
o
n
l
y
p
e
r
f
o
r
m
f
ea
tu
r
e
ex
tr
ac
tio
n
a
n
d
tr
an
s
f
o
r
m
atio
n
[
2
0
]
.
QC
NNs
tak
e
ad
v
an
ta
g
e
o
f
s
o
m
e
b
asic
q
u
an
tu
m
ch
ar
ac
ter
is
tics
,
in
clu
d
in
g
:
i)
Qu
an
tu
m
s
u
p
er
p
o
s
itio
n
:
a
s
a
q
u
an
tu
m
s
tate
ca
n
b
e
in
n
u
m
er
o
u
s
co
n
f
ig
u
r
atio
n
s
at
o
n
ce
,
QC
NNs
ca
n
s
im
u
ltan
eo
u
s
ly
ex
p
lo
r
e
d
if
f
e
r
e
n
t sets
o
f
f
ea
tu
r
e
r
e
p
r
esen
tatio
n
s
.
ii)
Qu
an
tu
m
e
n
tan
g
lem
en
t:
t
h
is
is
a
p
r
o
p
e
r
ty
th
at
ca
n
b
e
h
ar
n
ess
ed
to
cr
ea
te
s
tr
o
n
g
co
r
r
elatio
n
s
b
etwe
en
th
e
q
u
b
its
,
th
u
s
en
h
a
n
cin
g
f
ea
tu
r
e
in
ter
ac
tio
n
s
in
co
n
v
o
lu
tio
n
al
lay
er
s
.
iii)
Qu
an
tu
m
m
ea
s
u
r
em
en
t:
t
h
e
last
lay
er
o
f
a
QC
NN
tr
an
s
f
o
r
m
s
q
u
an
tu
m
s
tates
to
class
ica
l
in
f
o
r
m
atio
n
,
g
en
er
atin
g
i
n
tellig
ib
le
class
if
i
ca
tio
n
r
esu
lts
[
2
1
]
.
Ma
n
y
-
b
o
d
y
wav
e
f
u
n
ctio
n
s
h
o
ld
s
tr
u
ctu
r
al
r
elatio
n
s
o
f
a
class
o
f
m
ap
p
in
g
s
o
f
co
n
v
o
lu
tio
n
al
n
etwo
r
k
s
,
wh
ich
lead
s
to
a
n
aly
zin
g
th
e
tr
a
n
s
f
o
r
m
atio
n
class
if
icatio
n
-
ty
p
e
p
ar
ad
ig
m
o
f
th
e
p
h
y
s
ical
s
tate/en
s
u
r
in
g
th
e
p
h
ase
in
to
t
h
e
lear
n
in
g
tr
an
s
f
o
r
m
atio
n
o
f
t
r
ad
itio
n
al
im
ag
e
class
if
icatio
n
.
T
h
is
m
ea
n
s
th
at
it
is
n
ec
ess
ar
y
to
p
r
e
p
ar
e
q
u
an
t
u
m
in
itial
s
tates,
wh
er
e
th
e
h
ig
h
er
th
e
en
tan
g
led
s
tate,
th
e
h
ig
h
er
t
h
e
s
ep
ar
ate
d
weig
h
t f
u
n
ctio
n
[
2
2
]
.
T
h
e
q
u
a
n
tu
m
s
tate
tr
an
s
f
o
r
m
atio
n
ca
n
b
e
ex
p
r
ess
ed
as
in
(
1
)
.
|
0
⟩
=
∑
2
−
1
=
1
|
⟩
=
∑
2
−
1
=
0
|
(
1
)
W
h
er
e
∣
ψ0
⟩
is
th
e
n
o
tatio
n
f
o
r
th
e
in
itial
q
u
an
tu
m
s
tate,
∑
2
−
1
=
1
|
is
a
lin
ea
r
co
m
b
in
atio
n
o
f
b
asis
s
t
ates
∣
i
⟩
,
wh
er
e
i r
an
g
es f
r
o
m
0
to
2
n
−1
,
n
is
th
e
b
asis
s
tate
s
co
r
r
esp
o
n
d
in
g
to
a
p
o
s
s
ib
le
b
in
ar
y
s
tr
in
g
o
f
len
g
t
h
,
an
d
α
i,
an
d
βi
ar
e
co
m
p
lex
n
u
m
b
er
s
c
alled
am
p
litu
d
es,
ass
o
ciate
d
with
th
e
b
asis
s
tate
∣
i
⟩
[
2
3
]
.
Fig
u
r
e
2
illu
s
tr
ates
a
h
y
b
r
id
q
u
an
tu
m
-
class
ical
n
eu
r
al
n
et
wo
r
k
c
o
m
b
in
in
g
q
u
an
t
u
m
c
o
n
v
o
lu
tio
n
al
an
d
p
o
o
lin
g
lay
er
s
with
a
cla
s
s
ical
n
etwo
r
k
f
o
r
f
ea
tu
r
e
ex
t
r
ac
tio
n
an
d
class
if
icatio
n
.
C
lass
ical
d
ata
is
f
ir
s
t
em
b
ed
d
e
d
in
to
q
u
an
tu
m
s
tat
es
[
2
4
]
.
Qu
an
t
u
m
co
n
v
o
lu
tio
n
lay
er
s
ap
p
ly
u
n
itar
y
g
ates
to
en
tan
g
led
q
u
b
it
s
u
b
s
ets
in
s
u
p
er
p
o
s
itio
n
,
ca
p
t
u
r
in
g
r
ich
f
ea
tu
r
e
r
e
p
r
esen
tat
io
n
s
[
2
2
]
.
An
alo
g
o
u
s
to
clas
s
ical
p
o
o
lin
g
[
2
5
]
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J Ar
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I
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tell
I
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8
9
3
8
Hyb
r
id
q
u
a
n
t
u
m
-
cla
s
s
ica
l n
eu
r
a
l n
etw
o
r
ks fo
r
b
r
a
in
co
mp
u
ted
to
mo
g
r
a
p
h
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s
ca
n
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g
n
o
s
is
(
B
ila
l R
.
A
lta
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)
3345
q
u
an
tu
m
p
o
o
lin
g
r
ed
u
ce
s
q
u
b
it
co
u
n
t
b
y
d
is
ca
r
d
in
g
o
r
m
ea
s
u
r
in
g
less
in
f
o
r
m
ativ
e
q
u
b
its
[
2
6
]
.
T
h
e
r
esu
ltin
g
m
ea
s
u
r
em
en
ts
ar
e
th
en
p
ass
ed
to
a
class
ical
n
etwo
r
k
f
o
r
in
f
e
r
en
ce
[
2
7
]
,
co
m
b
in
in
g
q
u
an
tu
m
ex
p
r
ess
iv
ity
with
th
e
s
ca
lab
ilit
y
o
f
class
ical
d
ee
p
lear
n
in
g
.
Fig
u
r
e
2
.
QC
NN
ar
ch
itectu
r
e
[
1
0
]
3.
M
E
T
H
O
D
3
.
1
.
Da
t
a
s
et
T
h
e
d
ataset
o
n
wh
ich
th
e
QC
NN
m
o
d
el
is
d
o
wn
lo
ad
e
d
f
r
o
m
Kag
g
le
we
b
s
ite
(
b
ased
o
n
th
e
b
r
ain
co
m
p
u
ted
to
m
o
g
r
ap
h
y
(
C
T
)
s
ca
n
im
ag
es),
th
is
d
ataset
is
tr
ain
ed
,
test
ed
f
o
r
th
e
p
u
r
p
o
s
e
o
f
class
if
icatio
n
,
th
e
d
ata
is
class
if
ied
in
to
f
o
u
r
d
is
tin
ct
class
es
n
o
r
m
al,
h
em
o
r
r
h
a
g
e,
tu
m
o
r
,
an
d
s
tr
o
k
e
b
ased
o
n
th
e
b
r
ain
C
T
s
ca
n
im
ag
es.
T
h
e
d
is
tr
ib
u
tio
n
o
f
s
am
p
les
in
to
tr
ain
i
n
g
,
v
alid
atio
n
,
an
d
test
in
g
s
u
b
s
ets
f
o
r
ea
ch
class
f
ac
ilit
ates
th
e
m
o
d
el
d
e
v
elo
p
m
e
n
t,
tu
n
in
g
,
a
n
d
ev
alu
atio
n
p
r
o
ce
s
s
es.
i)
No
r
m
al
class
:
C
T
s
ca
n
s
o
f
h
ea
lth
y
b
r
ain
s
with
n
o
p
ath
o
l
o
g
ic
al
f
in
d
in
g
s
ar
e
in
cl
u
d
ed
i
n
th
is
ca
teg
o
r
y
as a
co
n
tr
o
l
g
r
o
u
p
.
T
h
ese
im
a
g
es
ar
e
v
er
y
im
p
o
r
tan
t
to
tr
ai
n
th
e
d
ee
p
er
lea
r
n
in
g
m
o
d
el
to
d
if
f
er
en
tiate
b
etwe
en
n
o
r
m
al
an
d
a
b
n
o
r
m
al
b
r
ain
co
n
d
itio
n
s
[
2
8
]
.
ii)
Hem
o
r
r
h
ag
e
class
:
th
is
class
in
clu
d
es
C
T
s
ca
n
s
d
em
o
n
s
tr
atin
g
m
u
ltip
le
ty
p
es o
f
i
n
tr
ac
r
an
i
al
h
em
o
r
r
h
ag
e
s
u
ch
as
s
u
b
ar
ac
h
n
o
i
d
,
i
n
tr
av
e
n
tr
icu
lar
,
s
u
b
d
u
r
al,
e
p
id
u
r
al,
a
n
d
in
tr
ap
ar
en
c
h
y
m
al
h
em
o
r
r
h
ag
e.
Acc
u
r
ate
id
en
tific
atio
n
o
f
h
em
o
r
r
h
a
g
e
t
y
p
es is
ess
en
tial f
o
r
p
r
o
m
p
t m
ed
ical
in
ter
v
en
tio
n
[
2
8
]
.
iii)
T
u
m
o
r
class
:
C
T
s
ca
n
s
in
th
i
s
class
ar
e
v
ar
io
u
s
b
r
ain
tu
m
o
r
ty
p
es,
in
clu
d
in
g
g
lio
m
as,
m
en
in
g
io
m
as,
an
d
p
itu
itar
y
tu
m
o
r
s
[
2
9
]
.
iv
)
Stro
k
e
class
:
th
is
cla
s
s
co
n
s
is
ts
o
f
C
T
s
ca
n
s
as
s
o
ciate
d
wit
h
ce
r
eb
r
o
v
ascu
lar
ac
cid
en
ts
t
h
at
p
r
im
ar
ily
in
v
o
lv
e
is
ch
em
ic
an
d
h
em
o
r
r
h
ag
ic
s
tr
o
k
es
[
3
0
]
.
3
.
2
.
P
r
o
po
s
ed
hy
brid qu
a
nt
um
-
cla
s
s
ica
l c
o
nv
o
lutio
na
l neura
l net
wo
rk
T
h
e
p
r
o
p
o
s
ed
QC
NN
class
if
i
es
a
b
r
ain
p
atien
t'
s
C
T
s
ca
n
in
to
f
o
u
r
ca
teg
o
r
ies:
n
o
r
m
al
b
r
ain
,
b
r
ain
h
em
o
r
r
h
ag
e,
b
r
ain
s
tr
o
k
e,
an
d
b
r
ain
tu
m
o
r
to
im
p
r
o
v
e
C
NN’
s
class
if
icat
io
n
o
f
m
ed
ic
al
im
ag
es.
T
h
e
k
ey
co
n
ce
p
t u
n
itin
g
th
e
QC
NN
m
o
d
el
is
th
at
class
ical
lear
n
in
g
ca
n
b
e
m
ad
e
m
o
r
e
e
f
f
icien
t b
y
le
v
er
ag
in
g
q
u
a
n
tu
m
co
m
p
u
tatio
n
.
I
n
th
is
ap
p
r
o
ac
h
,
th
e
p
r
o
p
o
s
ed
m
o
d
el
c
o
n
s
is
ts
o
f
f
iv
e
s
tag
es,
n
a
m
ely
th
e
d
ata
lo
a
d
in
g
a
n
d
p
r
ep
r
o
ce
s
s
in
g
s
tag
e
.
Seco
n
d
,
d
ef
in
in
g
th
e
q
u
a
n
tu
m
m
o
d
el
s
tag
e.
T
h
ir
d
,
b
u
ild
in
g
th
e
h
y
b
r
id
QC
NN
m
o
d
el
s
tag
e.
T
h
en
,
co
m
p
ilin
g
an
d
tr
ain
in
g
th
e
m
o
d
el
s
tag
e.
Fin
ally
,
ev
alu
atin
g
th
e
m
o
d
el.
T
h
ese
s
tag
es
ar
e
r
ep
r
esen
ted
in
Fig
u
r
e
3
.
3
.
3
.
Da
t
a
lo
a
din
g
a
nd
prepr
o
ce
s
s
ing
Star
t
with
p
r
ep
a
r
in
g
th
e
d
ataset,
th
e
im
a
g
es
ar
e
s
p
lit
in
t
o
t
r
ain
in
g
,
v
alid
atio
n
,
an
d
test
d
ir
ec
to
r
ies,
ea
ch
im
ag
e
h
as
b
ee
n
r
esized
t
o
6
4
×
6
4
p
i
x
els,
th
ese
d
ata
ar
e
r
u
n
i
n
b
atch
es
o
f
3
2
,
wh
ich
is
b
etter
to
h
an
d
le
m
em
o
r
y
an
d
co
m
p
u
tatio
n
al
e
f
f
icien
cy
.
Data
is
lo
ad
ed
b
y
a
u
tili
ty
th
at
d
i
r
ec
tly
r
ea
d
s
th
e
im
ag
es
f
r
o
m
th
e
d
ir
ec
to
r
y
s
tr
u
ctu
r
e.
As
a
r
esu
l
ts
,
th
e
d
ataset
g
ets
s
h
u
f
f
le
d
b
y
s
p
ec
if
ic
i
n
s
tr
u
ctio
n
s
a
n
d
p
r
ep
ar
ed
f
o
r
tr
ain
in
g
s
ea
m
less
ly
.
T
h
e
im
ag
es
ar
e
n
o
r
m
alize
d
b
y
s
ca
lin
g
th
em
b
e
twee
n
0
to
1
.
No
r
m
aliza
tio
n
a
llo
ws
f
o
r
s
m
o
o
t
h
er
tr
ain
in
g
o
f
th
e
m
o
d
el
as
well
as
p
r
ev
en
ts
p
r
o
b
lem
s
th
at
co
u
ld
ar
is
e
d
u
e
to
d
if
f
er
in
g
p
ix
el
in
ten
s
ity
s
ca
le
s
.
L
astl
y
,
ap
p
ly
in
g
o
p
tim
izatio
n
,
ca
ch
in
g
,
in
ter
leav
e,
an
d
p
r
ef
etch
in
g
tech
n
iq
u
es
im
p
r
o
v
es
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
d
ata
p
ip
elin
e.
T
h
ese
ap
p
r
o
ac
h
es
lead
to
f
aster
d
ata
lo
a
d
in
g
d
u
r
in
g
tr
ain
i
n
g
d
u
e
to
c
ac
h
in
g
th
e
d
ata
i
n
m
em
o
r
y
a
n
d
c
r
ea
tin
g
f
u
tu
r
e
b
atch
es wh
ile
th
e
cu
r
r
en
t
o
n
e
is
p
r
o
ce
s
s
ed
.
3
.
4
.
Def
ini
ng
t
he
qu
a
ntum
m
o
del
T
h
e
p
r
o
p
o
s
ed
q
u
a
n
tu
m
m
o
d
el
b
eg
in
s
b
y
s
ettin
g
u
p
a
q
u
a
n
tu
m
d
ev
ice
s
im
u
lato
r
.
Acc
o
r
d
in
g
to
th
at,
a
d
ef
au
lt
s
im
u
lato
r
in
itializes
a
f
o
u
r
-
q
u
b
it
s
y
s
tem
,
r
ep
r
esen
t
in
g
a
s
im
u
lated
q
u
an
tu
m
cir
cu
it.
T
h
e
q
u
an
t
u
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
26
:
3
3
4
2
-
3
3
5
1
3346
cir
cu
it
is
th
e
h
ea
r
t
o
f
th
e
m
o
d
el.
I
n
itially
,
class
ical
in
p
u
t
d
ata
is
em
b
ed
d
ed
in
to
th
e
q
u
an
tu
m
s
y
s
tem
u
s
in
g
r
o
tatio
n
-
b
ased
en
co
d
in
g
o
r
an
g
le
em
b
ed
d
in
g
.
T
h
is
tr
an
s
f
o
r
m
s
class
ical
in
f
o
r
m
atio
n
in
to
q
u
an
tu
m
s
tates.
T
h
e
cir
cu
it a
p
p
lies
a
s
eq
u
en
ce
o
f
p
ar
am
eter
ized
q
u
an
tu
m
g
ates a
cr
o
s
s
a
s
er
ies o
f
lay
er
s
,
en
tan
g
lin
g
th
e
q
u
b
its
.
T
h
e
o
u
tp
u
t
o
f
th
e
cir
c
u
it
is
th
e
ex
p
ec
tatio
n
v
alu
es
o
f
th
e
Pau
li
-
Z
o
p
er
ato
r
,
wh
ic
h
ac
ts
as
th
e
m
ea
s
u
r
ab
le
o
u
tp
u
ts
f
o
r
p
r
o
ce
s
s
in
g
in
th
e
n
e
x
t
lay
er
s
.
T
o
in
co
r
p
o
r
ate
th
is
q
u
a
n
t
u
m
f
u
n
ctio
n
ality
in
a
m
ac
h
in
e
lear
n
in
g
wo
r
k
f
lo
w,
th
e
q
u
a
n
tu
m
cir
c
u
it
is
wr
ap
p
ed
in
a
Ker
as
co
m
p
atib
le
lay
er
.
T
h
is
m
ea
n
s
t
h
at
it
ca
n
b
e
u
s
ed
lik
e
an
y
o
t
h
er
lay
er
in
a
n
e
u
r
al
n
etwo
r
k
,
tr
ai
n
ab
le,
an
d
ab
s
tr
ac
tab
le
lik
e
an
y
class
ical
d
ee
p
lear
n
in
g
s
o
f
t
war
e
to
o
l.
T
h
is
lay
er
ac
ce
p
ts
in
p
u
ts
an
d
r
etu
r
n
s
o
u
tp
u
ts
th
at
m
atch
th
e
n
u
m
b
er
o
f
q
u
b
its
,
lin
k
in
g
th
e
q
u
an
tu
m
p
ar
t
a
n
d
class
ical
p
ar
t o
f
t
h
e
s
y
s
tem
.
Fig
u
r
e
3
.
Flo
wch
ar
t
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
QC
NN
m
o
d
el
3
.
5
.
B
uil
din
g
t
he
hy
brid q
ua
ntum
-
cla
s
s
ica
l c
o
nv
o
lutio
na
l neura
l net
wo
rk
mo
del
T
h
e
p
r
o
p
o
s
ed
h
y
b
r
id
QC
NN
is
b
ased
o
n
class
ical
lay
er
s
o
n
p
r
o
ce
s
s
ea
ch
im
ag
e.
T
h
e
ar
ch
i
tectu
r
e
is
co
m
p
o
s
ed
o
f
two
co
n
v
o
lu
tio
n
al
b
lo
ck
s
with
co
n
v
o
lu
tio
n
a
l
lay
er
s
,
m
ax
p
o
o
lin
g
,
b
atch
n
o
r
m
aliza
tio
n
,
an
d
d
r
o
p
o
u
t.
T
h
ese
lay
e
r
s
ca
p
tu
r
e
an
d
en
h
an
ce
th
e
s
p
atial
f
ea
tu
r
es
p
r
esen
t
i
n
th
e
in
p
u
t
d
ata.
T
h
is
will
f
latten
th
e
f
ea
tu
r
e
m
ap
s
an
d
p
ass
th
em
to
a
d
en
s
e
(
f
u
lly
co
n
n
ec
ted
)
la
y
e
r
th
at
p
r
e
p
ar
es th
e
d
ata
f
o
r
q
u
a
n
tu
m
p
r
o
ce
s
s
in
g
.
I
n
o
r
d
er
to
in
tr
o
d
u
ce
q
u
an
t
u
m
co
m
p
u
tatio
n
,
a
d
en
s
e
lay
er
is
in
clu
d
ed
to
m
a
p
th
e
class
ical
f
ea
tu
r
es
to
a
s
ize
co
m
p
atib
le
with
th
e
in
p
u
t
o
f
th
e
q
u
an
tu
m
cir
cu
it,
t
h
er
eb
y
m
atc
h
in
g
t
h
e
n
u
m
b
er
o
f
q
u
b
its
.
T
h
en
th
is
o
u
tp
u
t
is
p
ass
ed
,
wh
ich
is
th
e
n
p
ass
ed
d
o
wn
in
to
th
e
q
u
an
t
u
m
lay
er
ap
p
ly
i
n
g
q
u
a
n
tu
m
o
p
er
atio
n
s
.
T
h
e
lay
e
r
o
u
tp
u
ts
ex
p
ec
tatio
n
v
alu
es
as
q
u
an
tu
m
-
p
r
o
ce
s
s
ed
f
ea
tu
r
es.
At
th
e
en
d
,
th
e
m
o
d
el
r
etu
r
n
s
to
class
ical
lay
er
s
f
o
r
class
if
icatio
n
.
T
h
e
o
u
t
p
u
ts
o
f
th
e
q
u
an
tu
m
cir
c
u
it
ar
e
th
en
f
ed
to
a
n
o
th
er
d
en
s
e
lay
er
with
an
ac
tiv
atio
n
f
u
n
ctio
n
a
n
d
th
e
n
to
a
n
o
u
t
p
u
t
lay
er
to
class
if
y
th
e
in
p
u
t to
o
n
e
o
f
th
e
class
es u
s
in
g
a
s
o
f
tm
ax
f
u
n
ctio
n
.
3
.
6
.
Co
m
pil
ing
a
nd
t
ra
ini
ng
t
he
m
o
del
S
tar
ts
wi
th
ca
lib
r
atin
g
th
e
lear
n
in
g
r
ate
to
f
in
d
th
e
o
p
tim
u
m
f
o
r
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
.
T
h
er
e
ar
e
o
n
ly
a
ce
r
tain
n
u
m
b
er
o
f
ep
o
ch
s
,
test
ed
at
v
ar
io
u
s
lear
n
in
g
r
ates
u
s
in
g
a
lo
o
p
,
th
is
d
eter
m
in
es
th
e
r
ate
th
at
allo
ws
th
e
m
o
d
el
to
lear
n
a
n
d
av
o
id
o
v
er
s
h
o
o
tin
g
b
esid
e
co
n
v
er
g
in
g
s
lo
wly
.
An
ea
r
ly
s
to
p
p
in
g
m
ec
h
an
is
m
is
ap
p
lied
to
en
s
u
r
e
tr
ain
in
g
p
r
o
ce
ed
s
r
eliab
ly
with
o
u
t
o
v
er
f
itti
n
g
.
T
h
is
ca
llb
ac
k
will
tr
ac
k
t
h
e
m
o
d
el’
s
p
er
f
o
r
m
an
ce
o
n
d
ata
t
h
at
h
as
n
ev
er
s
ee
n
v
alid
atio
n
d
ata
an
d
s
to
p
t
r
ain
in
g
in
ca
s
e
th
at
th
e
p
er
f
o
r
m
an
ce
d
o
es
n
o
t
s
ee
m
to
b
e
im
p
r
o
v
ed
,
th
u
s
s
av
in
g
tim
e
an
d
also
p
r
ev
en
ti
n
g
th
e
m
o
d
el
f
r
o
m
“o
v
er
f
itti
n
g
”
(
m
em
o
r
izin
g
)
to
th
e
tr
ain
in
g
d
ata.
Fin
ally
,
th
er
e
is
a
s
tep
in
th
e
tr
ain
in
g
p
r
o
ce
s
s
o
f
s
av
in
g
th
e
b
est v
er
s
io
n
o
f
th
e
m
o
d
el.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
Hyb
r
id
q
u
a
n
t
u
m
-
cla
s
s
ica
l n
eu
r
a
l n
etw
o
r
ks fo
r
b
r
a
in
co
mp
u
ted
to
mo
g
r
a
p
h
y
s
ca
n
d
ia
g
n
o
s
is
(
B
ila
l R
.
A
lta
mer
)
3347
3
.
7
.
E
v
a
lua
t
ing
t
he
m
o
del
O
n
ce
tr
ain
in
g
is
d
o
n
e
,
th
e
m
o
d
el
is
ev
alu
ated
o
n
th
e
test
d
ataset
f
o
r
th
e
f
in
al
m
o
d
el
p
e
r
f
o
r
m
a
n
ce
s
co
r
e.
Oth
er
s
tatis
tic
s
s
u
ch
as
lo
s
s
,
ac
cu
r
ac
y
,
an
d
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
ar
e
also
ca
lcu
lated
to
p
r
o
v
id
e
in
f
o
r
m
atio
n
ab
o
u
t
th
e
m
o
d
el'
s
g
en
er
aliza
tio
n
ca
p
a
b
ilit
ies.
A
s
ec
o
n
d
an
aly
s
is
will
g
en
er
ate
a
class
if
icatio
n
r
ep
o
r
t,
wh
ich
s
h
o
ws
th
e
tr
u
e
lab
els
v
er
s
u
s
th
e
p
r
ed
icted
la
b
els.
T
h
is
r
ep
o
r
t
d
is
p
lay
s
p
r
e
cisi
o
n
s
,
r
ec
alls
,
an
d
F1
-
s
co
r
es
class
-
wis
e,
wh
ich
h
elp
s
to
g
ain
i
n
s
ig
h
t
in
t
o
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
m
o
d
el
an
d
its
wea
k
n
ess
es.
T
h
e
m
o
d
el
also
g
en
e
r
ates
a
co
n
f
u
s
io
n
m
atr
ix
,
wh
ic
h
p
lo
ts
in
f
o
r
m
atio
n
ab
o
u
t
th
e
ac
cu
r
ac
y
o
f
th
e
m
o
d
el
to
p
r
ed
ict
ea
ch
class
an
d
th
e
p
o
in
ts
wh
e
r
e
th
e
m
o
d
el
is
g
ettin
g
it
wr
o
n
g
.
T
h
e
n
r
ec
eiv
er
o
p
er
atin
g
c
h
ar
ac
ter
is
tic
(
R
OC
)
cu
r
v
e
f
o
r
ea
ch
class
an
d
ar
e
a
u
n
d
er
t
h
e
c
u
r
v
e
(
AUC)
is
p
lo
tted
to
s
ee
th
e
p
e
r
f
o
r
m
an
ce
o
f
th
e
m
o
d
el
in
d
is
tin
g
u
is
h
in
g
class
es.
Usi
n
g
t
h
e
tr
ain
in
g
d
ata,
th
e
p
lo
t
o
f
lo
s
s
an
d
ac
cu
r
ac
y
t
h
r
o
u
g
h
o
u
t
th
e
en
tire
t
r
ain
in
g
r
u
n
is
u
s
ed
to
id
en
tify
a
n
d
ev
al
u
ate
lear
n
in
g
tr
e
n
d
s
,
wh
ich
a
r
e
r
e
co
g
n
ized
as o
v
er
f
itti
n
g
o
r
u
n
d
er
f
itti
n
g
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
p
r
o
p
o
s
ed
QC
NN
m
o
d
el
h
as
b
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im
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ten
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iv
ely
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ate
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ain
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T
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n
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r
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ately
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esp
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ea
d
b
r
ain
d
is
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es.
T
h
e
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lts
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clu
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ed
d
if
f
er
en
t
p
er
f
o
r
m
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ce
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etr
ic
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ate
o
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r
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o
d
el,
i
n
clu
d
in
g
a
co
n
f
u
s
io
n
m
at
r
ix
,
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OC
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r
v
es,
a
class
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icatio
n
r
ep
o
r
t
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p
r
ec
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io
n
,
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ec
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F1
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s
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ac
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r
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r
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d
l
o
s
s
cu
r
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i
ch
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e
a
co
m
p
lete
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ictu
r
e
o
f
th
e
r
o
b
u
s
tn
ess
o
f
t
h
e
m
o
d
el.
Fig
u
r
e
4
s
h
o
ws
th
e
class
if
icatio
n
r
ep
o
r
t
in
clu
d
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o
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er
all
ac
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r
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s
s
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MA
E
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r
ec
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r
ec
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F
1
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s
co
r
e,
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d
s
u
p
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o
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t f
o
r
th
e
m
o
d
el.
Fig
u
r
e
4
.
C
lass
if
icatio
n
r
ep
o
r
t
o
f
th
e
p
r
o
p
o
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ed
QC
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m
o
d
el
T
h
e
co
n
f
u
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io
n
m
atr
ix
s
h
o
wn
in
Fig
u
r
e
5
d
em
o
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ates
e
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n
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e
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iag
n
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tic
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o
r
ies
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th
e
h
em
o
r
r
h
a
g
e
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r
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ied
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er
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ec
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ly
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ilit
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0
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o
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er
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r
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4
7
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ib
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5
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o
r
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u
r
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er
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th
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is
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im
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atin
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o
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h
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o
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les
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u
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im
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atio
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r
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teg
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g
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tu
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r
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o
r
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ly
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h
e
p
r
ec
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ely
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ig
h
ac
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ac
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ig
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ib
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r
e
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ter
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en
s
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d
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e
cif
icity
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ter
m
s
o
f
m
ed
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iag
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o
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u
r
ac
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d
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s
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r
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ar
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in
Fig
u
r
e
6
.
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h
e
ac
cu
r
ac
y
cu
r
v
e,
as
s
h
o
wn
in
Fig
u
r
e
6
(
a)
,
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tr
o
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g
ly
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n
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i
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m
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th
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lear
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ea
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est
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o
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m
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itio
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s
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h
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u
r
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6
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b
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u
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r
ts
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Evaluation Warning : The document was created with Spire.PDF for Python.