SlideShare a Scribd company logo
1 of 142
Download to read offline
Quantum Processes in Graph Computing
Dr. Marko A. Rodriguez
DataStax and Apache TinkerPop
Rodriguez, M.A., Watkins, J.H., "Quantum Walks with Gremlin," GraphDay '16, pp. 1-16, Austin, Texas, January 2016.
http://arxiv.org/abs/1511.06278
Part 1
Wave Dynamics in Graphs
0.0
-1.0
1.0
0.0
-1.0
1.0
0.0
-1.0
1.0
0.0
-1.0
1.0
0.0
-1.0
1.0
0.0
-1.0
1.0
0.0
-1.0
1.0
0.0
-1.0
1.0
0.0
-1.0
1.0
0.0
-1.0
1.0
0.0
-1.0
1.0
0.0
-1.0
1.0
0.0
-1.0
1.0
However, vertices don't "move."
The graph topology is the only space.
There is no "space" outside the graph.
"Then how do you model an oscillating vertex?"
By computing the amount of "energy"
at the vertex over time.
"But traversers are indivisible objects!
Energy must be able to be
divided indefinitely and take negative values."
Lets look at Gremlin Sacks.
Gremlin Sacks
(a data structure local to the traverser)
(each traverser can carry an object along its way)
The traverser is not divisible, but that
double floating point number in his sack is!
1.0
0.0
-1.0
1.0
Don't worry about the algorithm, just watch the visualization for now.
-0.5 0.5
0.0
-1.0
1.0
Don't worry about the algorithm, just watch the visualization for now.
-0.5 0.250.25
0.0
-1.0
1.0
Don't worry about the algorithm, just watch the visualization for now.
-0.125 0.125-0.3750.375
0.0
-1.0
1.0
Don't worry about the algorithm, just watch the visualization for now.
0.375-0.3125 -0.3125
0.0
-1.0
1.0
Don't worry about the algorithm, just watch the visualization for now.
0.15625 -0.156250.34375-0.34375
0.0
-1.0
1.0
Don't worry about the algorithm, just watch the visualization for now.
1 2 30 4 5 6
The graph.
1 2 30 4 5 6
1.0
g.withSack(1.0,sum).V(3).
repeat(
union(
sack(mult).by(-0.5).out('left'),
sack(mult).by(0.5).out('right')
)
).times(3)
An initial energy of 1.0 perturbs the graph.
1 2 30 4 5 6
g.withSack(1.0,sum).V(3).
repeat(
union(
sack(mult).by(-0.5).out('left'),
sack(mult).by(0.5).out('right')
)
).times(3)
-0.5 0.51.0
out('left') out('right')
The energy diffuses left and right along the graph.
1 2 30 4 5 6
g.withSack(1.0,sum).V(3).
repeat(
union(
sack(mult).by(-0.5).out('left'),
sack(mult).by(0.5).out('right')
)
).times(3)
-0.5 0.5
1
tim
e
The total energy in the system is always 1.
Energy waves reverberate and thus, prior "left energy" can go right.
1 2 30 4 5 6
g.withSack(1.0,sum).V(3).
repeat(
union(
sack(mult).by(-0.5).out('left'),
sack(mult).by(0.5).out('right')
)
).times(3)
-0.5 0.250.25 -0.5 0.5
out('left') out('right') out('left') out('right')
1 2 30 4 5 6
g.withSack(1.0,sum).V(3).
repeat(
union(
sack(mult).by(-0.5).out('left'),
sack(mult).by(0.5).out('right')
)
).times(3)
-0.5 0.250.25 -0.5 0.5
out('left') out('right') out('left') out('right')
(-0.5 * 0.5) + (0.5 * -0.5)
-0.5
-0.25 + -0.25
Energy constructively and destructively interferes.
1 2 30 4 5 6
g.withSack(1.0,sum).V(3).
repeat(
union(
sack(mult).by(-0.5).out('left'),
sack(mult).by(0.5).out('right')
)
).times(3)
-0.5 0.250.25
2
tim
es
1 2 30 4 5 6
g.withSack(1.0,sum).V(3).
repeat(
union(
sack(mult).by(-0.5).out('left'),
sack(mult).by(0.5).out('right')
)
).times(3)
-0.125 0.125-0.3750.375 -0.5 0.250.25
out('left') out('left') out('left')out('right') out('right') out('right')
1 2 30 4 5 6
g.withSack(1.0,sum).V(3).
repeat(
union(
sack(mult).by(-0.5).out('left'),
sack(mult).by(0.5).out('right')
)
).times(3)
3
tim
es
-0.125 0.125-0.3750.375
A wave is a diffusion of energy within a space.
The space is the graph.
The energy is contained within the traversers (via their sacks).
When a traverser splits, its sack energy is divided amongst its children
traversers' sacks.
When traversers merge, their sack energy is summed to a
single traverser sack.
If two energy sacks with the same parity are merged,
constructive interference.
If a negative and a positive energy sack are merged,
destructive interference.
Part 2
Quantum Mechanics
via the
Copenhagen Interpretation
1
2
3
0 4 5
Space = Graph
Particle = Traverser
1
2
3
0 4 5
gremlin> g.V(0)
==>v[0]
1
2
3
0 4 5
gremlin> g.V(0).out()
==>v[1]
1
2
3
0 4 5
gremlin> g.V(0).out().out()
==>??
?
?
"Two outgoing adjacent vertices,
what should I do?"
1
2
3
0 4 5
gremlin> g.V(0).out().out()
==>v[2]
==>v[3]
1
2
3
0 4 5
gremlin> g.V(0).out().out()
==>v[2]
==>v[3]
2 particles are created from 1.
In graph computing: "There are two legal paths, so take both."
In physical computing: "Violates conservation of energy law (matter is created)."
1
2
3
0 4 5
gremlin> g.V(0).out().out().out()
==>v[4]
==>v[4]
In graph computing: "Two length 3 paths lead to vertex 4."
In physical computing: "The twin particles coexist at vertex 4."
1
2
3
0 4 5
gremlin> g.V(0).out().out().out().out()
==>v[5]
==>v[5]
In graph computing: "Two length 3 paths lead to vertex 5."
In physical computing: "The twin particles coexist at vertex 5."
1
2
3
0 4 5
What does a physical particle do when it is faced with a choice?
?
?
1
2
3
0 4 5
The particle takes both options!
The indivisible "particle" divides!
1
2
3
0 4 5
Two particles can't be "seen," but we know both options are taken.
When we try to see both "particles", they instantly become one particle.
"Why do you say the particle took both paths?
If you find it only on a single path, then wasn't only one path taken?"
Wave interference!
Explanation coming soon...
"Wave Function Collapse"
The smallest "quanta" allowed is the particle.
Only one particle is ever directly observed.
However, prior to observation, the particle splits into pieces (a wave).
When we observe the system, the pieces becomes one again (a particle).
Other Interpretations of Quantum Mechanics
Multi-World Interpretation
Pilot Wave Interpretation
photon
electron
bacteria!
an isolated system
Part 3
The Quantum Wave Function
Classical Particle
(A single unique "thing" exists)
The Particle Diffuses over Space as a Wave
(The particle is in a quantum superposition)
The Wave Function is Perturbed
(e.g., light tries to "see" the particle)
The Wave Function Collapses to a Classical Particle
(The wave turns into a particle)
Classical Particle
(A single unique "thing" exists)
The Particle Diffuses over Space as a Wave
(The particle is in a quantum superposition)
The Wave Function is Perturbed
(e.g., light tries to "see" the particle)
The Wave Function Collapses to a Classical Particle
(The wave turns into a particle)
ObservedObservedInferred
48 49 5047 51 52 53
A 101 vertex line graph with the center being vertex 50.
Space
Classical Initial Step
The particle has a definite location and a spin/potential to the left.
48 49 5047 51 52 53
[1, 0]
[1,0]50
left-ness
right-ness
For each option (degree of freedom),
we need a vector component.
In the article, we call it "traversal superposition."
0.00.20.40.60.81.0
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
48 49 5047 51 52 53
The probability of seeing the particle at vertex 50 is 100%.
[1, 0]
12
+ 02
= 1
Wave Function to Probability Distribution
Coin Step
48 49 5047 51 52 53
H =
1
√
2
1 1
1 −1
The particle's spin is put into superposition.
[1, 0] →
1
√
2
,
1
√
2
Ithastheoptiontogo
leftorrightsoitgoesboth!
Coin Step
48 49 5047 51 52 53
H =
1
√
2
1 1
1 −1
The particle's spin is put into superposition.
[1, 0] →
1
√
2
,
1
√
2
"right particles" going right flip their sign.
"left particles" going left or right keep their sign.
Coin Step
48 49 5047 51 52 53
H =
1
√
2
1 1
1 −1
The particle's spin is put into superposition.
[1, 0] →
1
√
2
,
1
√
2
1
√
2
1 1
1 −1
· [1, 0] =
1√
2
1√
2
1√
2
− 1√
2
· [1, 0] =
1
√
2
,
1
√
2
Coin Step
48 49 5047 51 52 53
H =
1
√
2
1 1
1 −1
The particle's spin is put into superposition.
1 ·
1
√
2
+ 0 ·
1
√
2
, 1 ·
1
√
2
+ 0 · −
1
√
2
=
1
√
2
,
1
√
2
[1, 0] →
1
√
2
,
1
√
2
1
√
2
1 1
1 −1
· [1, 0] =
1√
2
1√
2
1√
2
− 1√
2
· [1, 0] =
1
√
2
,
1
√
2
Coin Step
48 49 5047 51 52 53
1
√
2
,
1
√
2
The "particle" spinning left goes left and
the "particle" spinning right goes right.
Shift Step
48 49 5047 51 52 53
1
√
2
, 0 0,
1
√
2
1
√
2
,
1
√
2
48 49 5047 51 52 53
1
√
2
, 0 0,
1
√
2
Shift Step
P(v) = |c0|2
+ |c1|2
48 49 5047 51 52 53
1
√
2
, 0 0,
1
√
2
1
√
2
2
+ 02
= 0.5 02
+
1
√
2
2
= 0.5
Wave Function to Probability Distribution
The probability of locating the classical particle each vertex
is a function of the total wave amplitude at that vertex.
50%-chance 50%-chance
0.00.10.20.30.40.5
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
48 49 5047 51 52 53
1
√
2
, 0 0,
1
√
2
1
√
2
2
+ 02
= 0.5 02
+
1
√
2
2
= 0.5
The probability of seeing the particle at vertex 49 or 51 is 50%.
Wave Function to Probability Distribution
48 49 5047 51 52 53
H =
1
√
2
1 1
1 −1
Each "particles'" spin is put into superposition.
Coin Step
1
2
,
1
2
1
2
, −
1
2
48 49 5047 51 52 53
Shift Step
1
2
,
1
2
The "particles" spinning left go left and
the "particles" spinning right go right.
0, −
1
2
1
2
, 0
1
2
,
1
2
1
2
, −
1
2
48 49 5047 51 52 53
Shift Step
1
2
,
1
2
0, −
1
2
1
2
, 0
Constructive Interference
0.00.10.20.30.40.5
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
48 49 5047 51 52 53
1
2
,
1
2
0, −
1
2
1
2
, 0
1
2
2
+ 02
=
1
4
1
2
2
+
1
2
2
=
1
2
02
+ −
1
2
2
=
1
4
Wave Function to Probability Distribution
48 49 5047 51 52 53
Coin Step
H =
1
√
2
1 1
1 −1
The "particles'" spin is put into superposition.
1
2
√
2
,
1
2
√
2
−
1
2
√
2
,
1
2
√
2
1
√
2
, 0
48 49 5047 51 52 53
Shift Step
The "particles" spinning left go left and
the "particles" spinning right go right.
1
2
√
2
, 0
1
√
2
,
1
2
√
2
−
1
2
√
2
, 0 0,
1
2
√
2
Destructive InterferenceConstructive Interference
Wave Function to Probability Distribution
0.00.10.20.30.40.50.6
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
48 49 5047 51 52 53
1
2
√
2
, 0
1
√
2
,
1
2
√
2
−
1
2
√
2
, 0 0,
1
2
√
2
1
2
√
2
2
+ 02
=
1
8
02
+
1
2
√
2
2
=
1
8
−
1
2
√
2
2
+ 02
=
1
8
1
√
2
2
+
1
2
√
2
2
=
5
8
0.00.10.20.30.40.50.6
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
48 49 5047 51 52 53
0.00.10.20.30.40.5
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
48 49 5047 51 52 53
0.00.10.20.30.4
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
48 49 5047 51 52 53
0.000.050.100.150.200.250.30
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
48 49 5047 51 52 53
0.00.10.20.30.4
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
48 49 5047 51 52 53
0.00.10.20.30.4
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
48 49 5047 51 52 53
0.00.10.20.30.4
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
48 49 5047 51 52 53
0.00.10.20.30.4
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
48 49 5047 51 52 53
0.000.050.100.150.200.250.30
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
48 49 5047 51 52 53
0.000.050.100.150.200.25
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
48 49 5047 51 52 53
0.000.050.100.150.200.250.30
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
48 49 5047 51 52 53
0.00.10.20.3
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
48 49 5047 51 52 53
Fast forward to iteration 50.
0.000.050.100.15
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function to Probability Distribution
13 14 1512 16 17 18
0.000.050.100.15
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function is Perturbed by Light
13 14 1512 16 17 18
0.000.050.100.15
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function Collapse
13 14 1512 16 17 18
sample
Classical Particle
13 14 1512 16 17 18
[1, 0]
0.00.20.40.60.81.0
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
0.000.050.100.15
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function Collapse
13 14 1512 16 17 18
Could
have
been
thisvertex.
Again,itsa
probabilitydistribution.
0.000.050.100.15
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
Wave Function Collapse
13 14 1512 16 17 18
Could not be at this vertex because there
is no wave energy at that vertex.
g.withSack([1,0],sackSum).V(50).
repeat(
sack(hadamard).
union(
sack(project).by(constant([1,0])).out('left'),
sack(project).by(constant([0,1])).out('right')
)
).times(50).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
48 49 5047 51 52 53
48 49 5047 51 52 53
[1, 0]
g.withSack([1,0],sackSum).V(50).
repeat(
sack(hadamard).
union(
sack(project).by(constant([1,0])).out('left'),
sack(project).by(constant([0,1])).out('right')
)
).times(50).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
Classical Initial Step
sackSum = { a,b -> [a[0] + b[0],a[1] + b[1]] }
hadamard = { a,b ->
[(1/Math.sqrt(2)) * (a[0] + a[1]), (1/Math.sqrt(2)) * (a[0] - a[1])]
}
g.withSack([1,0],sackSum).V(50).
repeat(
sack(hadamard).
union(
sack(project).by(constant([1,0])).out('left'),
sack(project).by(constant([0,1])).out('right')
)
).times(50).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
Coin Step
H =
1
√
2
1 1
1 −1
48 49 5047 51 52 53
1
√
2
,
1
√
2
project = { a,b -> [a[0] * b[0], a[1] * b[1]] }
g.withSack([1,0],sackSum).V(50).
repeat(
sack(hadamard).
union(
sack(project).by(constant([1,0])).out('left'),
sack(project).by(constant([0,1])).out('right')
)
).times(50).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
Shift Step
48 49 5047 51 52 53
1
√
2
, 0 0,
1
√
2
1
√
2
,
1
√
2
g.withSack([1,0],sackSum).V(50).
repeat(
sack(hadamard).
union(
sack(project).by(constant([1,0])).out('left'),
sack(project).by(constant([0,1])).out('right')
)
).times(50).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
Diffuse the wave function for 50 steps
48 49 5047 51 52 53
0.000.050.100.15
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
norm = { Math.pow(it.get()[0],2) +
Math.pow(it.get()[1],2) }
g.withSack([1,0],sackSum).V(50).
repeat(
sack(hadamard).
union(
sack(project).by(constant([1,0])).out('left'),
sack(project).by(constant([0,1])).out('right')
)
).times(50).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
Wave Function to Probability Distribution
13 14 1512 16 17 18
P(v) = |c0|2
+ |c1|2
g.withSack([1,0],sackSum).V(50).
repeat(
sack(hadamard).
union(
sack(project).by(constant([1,0])).out('left'),
sack(project).by(constant([0,1])).out('right')
)
).times(50).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
Wave Function Collapse (Classical Particle Manifested)
13 14 1512 16 17 18
0.000.050.100.15
vertices
probability
0 4 8 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97
sample
g.withSack([1,0],sackSum).V(50).
repeat(
sack(hadamard).
union(
sack(project).by(constant([1,0])).out('left'),
sack(project).by(constant([0,1])).out('right')
)
).times(50).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
A Quantum Walk on a Line with Gremlin
H =
1
√
2
1 1
1 −1
Y =
1
√
2
1 i
i 1
Balanced Unitary OperatorUnbalanced Unitary Operator
0.000.050.100.15
vertices
probability
0 6 12 20 28 36 44 52 60 68 76 84 92 100
0.000.020.040.060.080.10
vertices
probability
0 6 12 20 28 36 44 52 60 68 76 84 92 100
Any energy split operator can be used as long as:
1. The operation conserves the total energy in the system.
2. It is possible to reverse the operation.
In general, a unitary operator is used to diffuse energy.
complexnumbers
i2
=−1
U · U∗
= I
nofriction
noinformation
loss
The probability of seeing the particle at a particular vertex after
50 iterations for both a quantum walk and a classical walk.
Quantum walks can explore more graph in a shorter amount of time!
0.000.020.040.060.080.10
vertices
probability
0 6 12 20 28 36 44 52 60 68 76 84 92 100
√
50 = 7.07
50 = 50
√
n
n
Y =
1
√
2
1 i
i 1
Part 4
The Double Slit Experiment
Thomas Young (1773-1829)
Question
"Is light a wave or a particle?"
continuous light source double-slit screen photoelectric film
Thomas Young (1773-1829)
Hypothesis
"If light is a wave, then the film will show a
constructive and destructive interference pattern."
continuous light source double-slit screen photoelectric film
Thomas Young (1773-1829)
Result
"Light must be a wave because it has interference and
refraction patterns like other natural waves such as
water waves."
continuous light source double-slit screen photoelectric film
G. I. Taylor (1886-1975)
single photon light source double-slit screen photoelectric film
Question
"What happens when only a single photon
of light is emitted?"
The most indivisible amount of light.
G. I. Taylor (1886-1975)
single photon light source double-slit screen photoelectric film
Hypothesis
"The photons must act like bullets
and hit directly behind the slits."
50%
chance
50% chance
G. I. Taylor (1886-1975)
single photon light source double-slit screen photoelectric film
Experiment
"Shoot photon #1!"
G. I. Taylor (1886-1975)
single photon light source double-slit screen photoelectric film
Experiment
"Shoot photon #2!"
G. I. Taylor (1886-1975)
single photon light source double-slit screen photoelectric film
Experiment
"Shoot photon #3!"
G. I. Taylor (1886-1975)
single photon light source double-slit screen photoelectric film
Experiment
"Shoot photon #4!"
Fast forward to photon number 20.
G. I. Taylor (1886-1975)
single photon light source double-slit screen photoelectric film
Experiment
"Shoot photon #20!"
G. I. Taylor (1886-1975)
single photon light source double-slit screen photoelectric film
Result
"A single quantum of light (a photon) turns into a
wave after emission and then is localized back to a
particle at the film based on the wave function."
single photon light source
double-slit screen
photoelectric film
Classical particle.
Particle becomes a wave.
"Particle" takes both options.
Wave interference.
Probability distribution generated from the wave function.
Wave function collapses back to a classical particle.
0.0e+001.0e-052.0e-05
screen vertices
probability
0 2 4 6 8 10 12 14 16 18 20
Com
puted
using
G
rem
lin
g.withSack([0,0,1,0],sackSum).V(10).
repeat(
sack(grover).
union(
choose(out('left'),
out('left').sack(project).by(constant([1,0,0,0])),
identity().sack(project).by(constant([1,0,0,0])).
sack(flip).by(constant("lr"))),
choose(out('right'),
out('right').sack(project).by(constant([0,1,0,0])),
identity().sack(project).by(constant([0,1,0,0])).
sack(flip).by(constant("lr"))),
choose(out('up'),
out('up').sack(project).by(constant([0,0,1,0])),
identity().sack(project).by(constant([0,0,1,0])).
sack(flip).by(constant("ud"))),
choose(out('down'),
out('down').sack(project).by(constant([0,0,0,1])),
identity().sack(project).by(constant([0,0,0,1])).
sack(flip).by(constant("ud"))))).
times(22).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
g.withSack([0,0,1,0],sackSum).V(10).
repeat(
sack(grover).
union(
choose(out('left'),
out('left').sack(project).by(constant([1,0,0,0])),
identity().sack(project).by(constant([1,0,0,0])).
sack(flip).by(constant("lr"))),
choose(out('right'),
out('right').sack(project).by(constant([0,1,0,0])),
identity().sack(project).by(constant([0,1,0,0])).
sack(flip).by(constant("lr"))),
choose(out('up'),
out('up').sack(project).by(constant([0,0,1,0])),
identity().sack(project).by(constant([0,0,1,0])).
sack(flip).by(constant("ud"))),
choose(out('down'),
out('down').sack(project).by(constant([0,0,0,1])),
identity().sack(project).by(constant([0,0,0,1])).
sack(flip).by(constant("ud"))))).
times(22).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
Four degrees of freedom means you have a four entry energy vector.
g.withSack([0,0,1,0],sackSum).V(10).
repeat(
sack(grover).
union(
choose(out('left'),
out('left').sack(project).by(constant([1,0,0,0])),
identity().sack(project).by(constant([1,0,0,0])).
sack(flip).by(constant("lr"))),
choose(out('right'),
out('right').sack(project).by(constant([0,1,0,0])),
identity().sack(project).by(constant([0,1,0,0])).
sack(flip).by(constant("lr"))),
choose(out('up'),
out('up').sack(project).by(constant([0,0,1,0])),
identity().sack(project).by(constant([0,0,1,0])).
sack(flip).by(constant("ud"))),
choose(out('down'),
out('down').sack(project).by(constant([0,0,0,1])),
identity().sack(project).by(constant([0,0,0,1])).
sack(flip).by(constant("ud"))))).
times(22).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
Bottom-middle vertex. The single photon light source.
A 4x4 unitary operator called the Grover coin. R =
1
2




−1 1 1 1
1 −1 1 1
1 1 −1 1
1 1 1 −1




g.withSack([0,0,1,0],sackSum).V(10).
repeat(
sack(grover).
union(
choose(out('left'),
out('left').sack(project).by(constant([1,0,0,0])),
identity().sack(project).by(constant([1,0,0,0])).
sack(flip).by(constant("lr"))),
choose(out('right'),
out('right').sack(project).by(constant([0,1,0,0])),
identity().sack(project).by(constant([0,1,0,0])).
sack(flip).by(constant("lr"))),
choose(out('up'),
out('up').sack(project).by(constant([0,0,1,0])),
identity().sack(project).by(constant([0,0,1,0])).
sack(flip).by(constant("ud"))),
choose(out('down'),
out('down').sack(project).by(constant([0,0,0,1])),
identity().sack(project).by(constant([0,0,0,1])).
sack(flip).by(constant("ud"))))).
times(22).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
g.withSack([0,0,1,0],sackSum).V(10).
repeat(
sack(grover).
union(
choose(out('left'),
out('left').sack(project).by(constant([1,0,0,0])),
identity().sack(project).by(constant([1,0,0,0])).
sack(flip).by(constant("lr"))),
choose(out('right'),
out('right').sack(project).by(constant([0,1,0,0])),
identity().sack(project).by(constant([0,1,0,0])).
sack(flip).by(constant("lr"))),
choose(out('up'),
out('up').sack(project).by(constant([0,0,1,0])),
identity().sack(project).by(constant([0,0,1,0])).
sack(flip).by(constant("ud"))),
choose(out('down'),
out('down').sack(project).by(constant([0,0,0,1])),
identity().sack(project).by(constant([0,0,0,1])).
sack(flip).by(constant("ud"))))).
times(22).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
Diffuse the wave in all four directions on the lattice.
g.withSack([0,0,1,0],sackSum).V(10).
repeat(
sack(grover).
union(
choose(out('left'),
out('left').sack(project).by(constant([1,0,0,0])),
identity().sack(project).by(constant([1,0,0,0])).
sack(flip).by(constant("lr"))),
choose(out('right'),
out('right').sack(project).by(constant([0,1,0,0])),
identity().sack(project).by(constant([0,1,0,0])).
sack(flip).by(constant("lr"))),
choose(out('up'),
out('up').sack(project).by(constant([0,0,1,0])),
identity().sack(project).by(constant([0,0,1,0])).
sack(flip).by(constant("ud"))),
choose(out('down'),
out('down').sack(project).by(constant([0,0,0,1])),
identity().sack(project).by(constant([0,0,0,1])).
sack(flip).by(constant("ud"))))).
times(22).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
If there is a "wall," reflect the energy back. No decoherence/friction.
The
article
presents
the
details
around
reflection.
g.withSack([0,0,1,0],sackSum).V(10).
repeat(
sack(grover).
union(
choose(out('left'),
out('left').sack(project).by(constant([1,0,0,0])),
identity().sack(project).by(constant([1,0,0,0])).
sack(flip).by(constant("lr"))),
choose(out('right'),
out('right').sack(project).by(constant([0,1,0,0])),
identity().sack(project).by(constant([0,1,0,0])).
sack(flip).by(constant("lr"))),
choose(out('up'),
out('up').sack(project).by(constant([0,0,1,0])),
identity().sack(project).by(constant([0,0,1,0])).
sack(flip).by(constant("ud"))),
choose(out('down'),
out('down').sack(project).by(constant([0,0,0,1])),
identity().sack(project).by(constant([0,0,0,1])).
sack(flip).by(constant("ud"))))).
times(22).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
As before, generate the probability distribution and sample it (i.e. collapse the wave form).
g.withSack([0,0,1,0],sackSum).V(10).
repeat(
sack(grover).
union(
choose(out('left'),
out('left').sack(project).by(constant([1,0,0,0])),
identity().sack(project).by(constant([1,0,0,0])).
sack(flip).by(constant("lr"))),
choose(out('right'),
out('right').sack(project).by(constant([0,1,0,0])),
identity().sack(project).by(constant([0,1,0,0])).
sack(flip).by(constant("lr"))),
choose(out('up'),
out('up').sack(project).by(constant([0,0,1,0])),
identity().sack(project).by(constant([0,0,1,0])).
sack(flip).by(constant("ud"))),
choose(out('down'),
out('down').sack(project).by(constant([0,0,0,1])),
identity().sack(project).by(constant([0,0,0,1])).
sack(flip).by(constant("ud"))))).
times(22).
group().by(id).by(sack().map(norm)).
unfold().sample(1).by(values)
A Quantum Walk on a Bounded Lattice with Gremlin
Step 0
x-axis vertices
y-axisvertices
5
10
15
20
5 10 15 20
Step 22
0.0e+001.0e-052.0e-05
screen vertices
probability
0 2 4 6 8 10 12 14 16 18 20
x-axis vertices
y-axisvertices
5
10
15
20
5 10 15 20
Step 22
Part 5
Quantum Computing
In classical computing, a bit is either 0 or 1.
0
[1, 0]
1
In quantum computing, a qubit can be 0, 1, or a superposition of both.
0
[0, 1]
1
1
√
2
, −
1
√
2
NOT
In classical computing, a gate takes a {0,1}* and outputs {0,1}*.
0 1
In quantum computing, a quantum gate takes a superposition of {0,1}*
and outputs a superposition of {0,1}*.
NOT
X =
0 1
1 0
1
√
2
, −
1
√
2
−
1
√
2
,
1
√
2
f(0)→
1
f(0,1)→
1,0
Two
function
evaluations
forthe
costofone!
U
Every quantum gate is a unitary operator.
U · U∗
= I
Not all gates have to be the same operator.
YH
H =
1
√
2
1 1
1 −1
Y =
1
√
2
1 i
i 1
H Y RH
Gates are connected by "wires."
R =
1
2




−1 1 1 1
1 −1 1 1
1 1 −1 1
1 1 1 −1




R
2-qubits!
Its all just vertices and
edges!
A quantum algorithm (circuit) can evaluate
all possible inputs at once.breadth-first search
depth-first search
The algorithm's purpose is to guide the wave function to yield the
correct answer to the problem with a high-probability (near 100%) at
the time of sampling (i.e. wave function collapse).
5-qubit register, where each qubit is both a 0 and 1 (thus, all 32 possible states at once).
1 0 1 0 0
We can never observe a superposition of outputs.
Thus, our quantum algorithms must ensure wave collapse to the correct classical state.
* Read about the pilot-wave interpretation of quantum mechanics.
Breadth-first memory requirements not needed!
* Read about the multi-world interpretation of quantum mechanics.
Depth-first speed enabled!
breadth-first search
depth-first search
1 0 1 0 0
0.03
0
1
0
1
All 32 states/values exist simultaneously.
Only one state/value can exist at observation.
1/32
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
25
=
32
Total wave energy is split equally amongst all states/values.
If the wave function collapses at this point in time, only one number (0-31) will manifest itself in reality with each being equally likely.
A quantum algorithm leverages simultaneous states, but can ultimately only yield one output state (a collapsed wave).
Thus, if you don't want a random answer, the algorithm must focus the wave energy on the correct solution.
Waves are controlled via constructive and destructive interference as the various parallel computations can interact with one another.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
1 0 1 0 0
+
Quantum Circuit
Graph
Quantum Wave Function
Traversal
Quantum Graph Computing
Structure Process
U
U
U
Fin.
Rodriguez, M.A., Watkins, J.H., "Quantum Walks with Gremlin," GraphDay '16, pp. 1-16, Austin, Texas, January 2016.
http://arxiv.org/abs/1511.06278

More Related Content

Viewers also liked

Cassandra Virtual Node talk
Cassandra Virtual Node talkCassandra Virtual Node talk
Cassandra Virtual Node talk
Patrick McFadin
 
Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices
Presto: Distributed Machine Learning and Graph Processing with Sparse MatricesPresto: Distributed Machine Learning and Graph Processing with Sparse Matrices
Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices
Qian Lin
 
Real World Tales of Repair (Alexander Dejanovski, The Last Pickle) | Cassandr...
Real World Tales of Repair (Alexander Dejanovski, The Last Pickle) | Cassandr...Real World Tales of Repair (Alexander Dejanovski, The Last Pickle) | Cassandr...
Real World Tales of Repair (Alexander Dejanovski, The Last Pickle) | Cassandr...
DataStax
 
Configurando o Geany para Python - 03/2012
Configurando o Geany para Python - 03/2012Configurando o Geany para Python - 03/2012
Configurando o Geany para Python - 03/2012
Marco Mendes
 
Configurando o geany_para_python
Configurando o geany_para_pythonConfigurando o geany_para_python
Configurando o geany_para_python
Marco Mendes
 
Epiphany: Connecting Millions of Events to Thirty Billion Data Points in Real...
Epiphany: Connecting Millions of Events to Thirty Billion Data Points in Real...Epiphany: Connecting Millions of Events to Thirty Billion Data Points in Real...
Epiphany: Connecting Millions of Events to Thirty Billion Data Points in Real...
DataWorks Summit
 

Viewers also liked (20)

Titan: The Rise of Big Graph Data
Titan: The Rise of Big Graph DataTitan: The Rise of Big Graph Data
Titan: The Rise of Big Graph Data
 
Faunus: Graph Analytics Engine
Faunus: Graph Analytics EngineFaunus: Graph Analytics Engine
Faunus: Graph Analytics Engine
 
Titan: Big Graph Data with Cassandra
Titan: Big Graph Data with CassandraTitan: Big Graph Data with Cassandra
Titan: Big Graph Data with Cassandra
 
Graph Processing with Apache TinkerPop
Graph Processing with Apache TinkerPopGraph Processing with Apache TinkerPop
Graph Processing with Apache TinkerPop
 
Gremlin: A Graph-Based Programming Language
Gremlin: A Graph-Based Programming LanguageGremlin: A Graph-Based Programming Language
Gremlin: A Graph-Based Programming Language
 
Cassandra Virtual Node talk
Cassandra Virtual Node talkCassandra Virtual Node talk
Cassandra Virtual Node talk
 
HBaseCon 2015: S2Graph - A Large-scale Graph Database with HBase
HBaseCon 2015: S2Graph - A Large-scale Graph Database with HBaseHBaseCon 2015: S2Graph - A Large-scale Graph Database with HBase
HBaseCon 2015: S2Graph - A Large-scale Graph Database with HBase
 
F8 tech talk_pinterest_v4
F8 tech talk_pinterest_v4F8 tech talk_pinterest_v4
F8 tech talk_pinterest_v4
 
Seda an architecture for well-conditioned scalable internet services
Seda   an architecture for well-conditioned scalable internet servicesSeda   an architecture for well-conditioned scalable internet services
Seda an architecture for well-conditioned scalable internet services
 
Facebook's TAO & Unicorn data storage and search platforms
Facebook's TAO & Unicorn data storage and search platformsFacebook's TAO & Unicorn data storage and search platforms
Facebook's TAO & Unicorn data storage and search platforms
 
Data Driven Growth
Data Driven GrowthData Driven Growth
Data Driven Growth
 
IDEs y Frameworks mas utilizados
IDEs y Frameworks mas utilizadosIDEs y Frameworks mas utilizados
IDEs y Frameworks mas utilizados
 
Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices
Presto: Distributed Machine Learning and Graph Processing with Sparse MatricesPresto: Distributed Machine Learning and Graph Processing with Sparse Matrices
Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices
 
Real World Tales of Repair (Alexander Dejanovski, The Last Pickle) | Cassandr...
Real World Tales of Repair (Alexander Dejanovski, The Last Pickle) | Cassandr...Real World Tales of Repair (Alexander Dejanovski, The Last Pickle) | Cassandr...
Real World Tales of Repair (Alexander Dejanovski, The Last Pickle) | Cassandr...
 
Configurando o Geany para Python - 03/2012
Configurando o Geany para Python - 03/2012Configurando o Geany para Python - 03/2012
Configurando o Geany para Python - 03/2012
 
Configurando o geany_para_python
Configurando o geany_para_pythonConfigurando o geany_para_python
Configurando o geany_para_python
 
Introduction to cassandra 2014
Introduction to cassandra 2014Introduction to cassandra 2014
Introduction to cassandra 2014
 
Epiphany: Connecting Millions of Events to Thirty Billion Data Points in Real...
Epiphany: Connecting Millions of Events to Thirty Billion Data Points in Real...Epiphany: Connecting Millions of Events to Thirty Billion Data Points in Real...
Epiphany: Connecting Millions of Events to Thirty Billion Data Points in Real...
 
Deletes Without Tombstones or TTLs (Eric Stevens, ProtectWise) | Cassandra Su...
Deletes Without Tombstones or TTLs (Eric Stevens, ProtectWise) | Cassandra Su...Deletes Without Tombstones or TTLs (Eric Stevens, ProtectWise) | Cassandra Su...
Deletes Without Tombstones or TTLs (Eric Stevens, ProtectWise) | Cassandra Su...
 
HBaseCon 2015 General Session: Zen - A Graph Data Model on HBase
HBaseCon 2015 General Session: Zen - A Graph Data Model on HBaseHBaseCon 2015 General Session: Zen - A Graph Data Model on HBase
HBaseCon 2015 General Session: Zen - A Graph Data Model on HBase
 

Similar to Quantum Processes in Graph Computing

Perfect method for Frames
Perfect method for FramesPerfect method for Frames
Perfect method for Frames
andreslahe
 
Bp219 04-13-2011
Bp219 04-13-2011Bp219 04-13-2011
Bp219 04-13-2011
waddling
 

Similar to Quantum Processes in Graph Computing (20)

22PH102_ISE_U2_LP1_-_Notes.pdf
22PH102_ISE_U2_LP1_-_Notes.pdf22PH102_ISE_U2_LP1_-_Notes.pdf
22PH102_ISE_U2_LP1_-_Notes.pdf
 
Discussion on Quantum Entanglement.pdf
Discussion on Quantum Entanglement.pdfDiscussion on Quantum Entanglement.pdf
Discussion on Quantum Entanglement.pdf
 
Graphs of the Sine and Cosine Functions Lecture
Graphs of the Sine and Cosine Functions LectureGraphs of the Sine and Cosine Functions Lecture
Graphs of the Sine and Cosine Functions Lecture
 
The Many Worlds of Quantum Mechanics
The Many Worlds of Quantum MechanicsThe Many Worlds of Quantum Mechanics
The Many Worlds of Quantum Mechanics
 
Wave Motion Theory Part1
Wave Motion Theory Part1Wave Motion Theory Part1
Wave Motion Theory Part1
 
Perfect method for Frames
Perfect method for FramesPerfect method for Frames
Perfect method for Frames
 
Simple Matrix Factorization for Recommendation in Mahout
Simple Matrix Factorization for Recommendation in MahoutSimple Matrix Factorization for Recommendation in Mahout
Simple Matrix Factorization for Recommendation in Mahout
 
Semi-Classical Transport Theory.ppt
Semi-Classical Transport Theory.pptSemi-Classical Transport Theory.ppt
Semi-Classical Transport Theory.ppt
 
Kinematics final
Kinematics finalKinematics final
Kinematics final
 
Learning object #2
Learning object #2Learning object #2
Learning object #2
 
Chapter 6 taylor and maclaurin series
Chapter 6 taylor and maclaurin seriesChapter 6 taylor and maclaurin series
Chapter 6 taylor and maclaurin series
 
4 stochastic processes
4 stochastic processes4 stochastic processes
4 stochastic processes
 
Bp219 04-13-2011
Bp219 04-13-2011Bp219 04-13-2011
Bp219 04-13-2011
 
1010n3a
1010n3a1010n3a
1010n3a
 
Quantum Experiments
Quantum ExperimentsQuantum Experiments
Quantum Experiments
 
Chapter 11_0 velocity, acceleration.pdf
Chapter 11_0 velocity, acceleration.pdfChapter 11_0 velocity, acceleration.pdf
Chapter 11_0 velocity, acceleration.pdf
 
Artificial intelligence ai choice mechanism hypothesis of a mathematical method
Artificial intelligence ai choice mechanism hypothesis of a mathematical methodArtificial intelligence ai choice mechanism hypothesis of a mathematical method
Artificial intelligence ai choice mechanism hypothesis of a mathematical method
 
Numerical Differentiation and Integration
 Numerical Differentiation and Integration Numerical Differentiation and Integration
Numerical Differentiation and Integration
 
Laplace equation
Laplace equationLaplace equation
Laplace equation
 
Nuclear Decay - A Mathematical Perspective
Nuclear Decay - A Mathematical PerspectiveNuclear Decay - A Mathematical Perspective
Nuclear Decay - A Mathematical Perspective
 

More from Marko Rodriguez

Memoirs of a Graph Addict: Despair to Redemption
Memoirs of a Graph Addict: Despair to RedemptionMemoirs of a Graph Addict: Despair to Redemption
Memoirs of a Graph Addict: Despair to Redemption
Marko Rodriguez
 
The Network Data Structure in Computing
The Network Data Structure in ComputingThe Network Data Structure in Computing
The Network Data Structure in Computing
Marko Rodriguez
 
A Model of the Scholarly Community
A Model of the Scholarly CommunityA Model of the Scholarly Community
A Model of the Scholarly Community
Marko Rodriguez
 

More from Marko Rodriguez (20)

mm-ADT: A Virtual Machine/An Economic Machine
mm-ADT: A Virtual Machine/An Economic Machinemm-ADT: A Virtual Machine/An Economic Machine
mm-ADT: A Virtual Machine/An Economic Machine
 
mm-ADT: A Multi-Model Abstract Data Type
mm-ADT: A Multi-Model Abstract Data Typemm-ADT: A Multi-Model Abstract Data Type
mm-ADT: A Multi-Model Abstract Data Type
 
Open Problems in the Universal Graph Theory
Open Problems in the Universal Graph TheoryOpen Problems in the Universal Graph Theory
Open Problems in the Universal Graph Theory
 
Gremlin 101.3 On Your FM Dial
Gremlin 101.3 On Your FM DialGremlin 101.3 On Your FM Dial
Gremlin 101.3 On Your FM Dial
 
The Path Forward
The Path ForwardThe Path Forward
The Path Forward
 
The Pathology of Graph Databases
The Pathology of Graph DatabasesThe Pathology of Graph Databases
The Pathology of Graph Databases
 
Traversing Graph Databases with Gremlin
Traversing Graph Databases with GremlinTraversing Graph Databases with Gremlin
Traversing Graph Databases with Gremlin
 
The Path-o-Logical Gremlin
The Path-o-Logical GremlinThe Path-o-Logical Gremlin
The Path-o-Logical Gremlin
 
The Gremlin in the Graph
The Gremlin in the GraphThe Gremlin in the Graph
The Gremlin in the Graph
 
Memoirs of a Graph Addict: Despair to Redemption
Memoirs of a Graph Addict: Despair to RedemptionMemoirs of a Graph Addict: Despair to Redemption
Memoirs of a Graph Addict: Despair to Redemption
 
Graph Databases: Trends in the Web of Data
Graph Databases: Trends in the Web of DataGraph Databases: Trends in the Web of Data
Graph Databases: Trends in the Web of Data
 
Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, and Reco...
Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, and Reco...Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, and Reco...
Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, and Reco...
 
A Perspective on Graph Theory and Network Science
A Perspective on Graph Theory and Network ScienceA Perspective on Graph Theory and Network Science
A Perspective on Graph Theory and Network Science
 
The Graph Traversal Programming Pattern
The Graph Traversal Programming PatternThe Graph Traversal Programming Pattern
The Graph Traversal Programming Pattern
 
The Network Data Structure in Computing
The Network Data Structure in ComputingThe Network Data Structure in Computing
The Network Data Structure in Computing
 
A Model of the Scholarly Community
A Model of the Scholarly CommunityA Model of the Scholarly Community
A Model of the Scholarly Community
 
General-Purpose, Internet-Scale Distributed Computing with Linked Process
General-Purpose, Internet-Scale Distributed Computing with Linked ProcessGeneral-Purpose, Internet-Scale Distributed Computing with Linked Process
General-Purpose, Internet-Scale Distributed Computing with Linked Process
 
Collective Decision Making Systems: From the Ideal State to Human Eudaimonia
Collective Decision Making Systems: From the Ideal State to Human EudaimoniaCollective Decision Making Systems: From the Ideal State to Human Eudaimonia
Collective Decision Making Systems: From the Ideal State to Human Eudaimonia
 
Distributed Graph Databases and the Emerging Web of Data
Distributed Graph Databases and the Emerging Web of DataDistributed Graph Databases and the Emerging Web of Data
Distributed Graph Databases and the Emerging Web of Data
 
An Overview of Data Management Paradigms: Relational, Document, and Graph
An Overview of Data Management Paradigms: Relational, Document, and GraphAn Overview of Data Management Paradigms: Relational, Document, and Graph
An Overview of Data Management Paradigms: Relational, Document, and Graph
 

Recently uploaded

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
 

Recently uploaded (20)

EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
Cyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdfCyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdf
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdfRansomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
 
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
Spring Boot vs Quarkus the ultimate battle - DevoxxUKSpring Boot vs Quarkus the ultimate battle - DevoxxUK
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 

Quantum Processes in Graph Computing