20130911 idc hpc_geordie_rose_final

The D-Wave Two
Dr. Geordie Rose
Founder and CTO, D-Wave
2:00PM September 11th 2013
@ IDC HPC User Forum, Boston, MA
Image from
http://www.nas.nasa.gov/quantum/quantumcomp.html

What I’m going to talk about
1. What we are doing (& why)
2. The machines
3. What the machines do
2 © Copyright 2013 D-Wave Systems Inc.

Quantum computation … will be the first
technology that allows useful tasks to be
performed in collaboration between
parallel universes.
David Deutsch @ TED 2005
Image from
http://conferences.ted.com/TEDGlobal2010/program/3 © Copyright 2013 D-Wave Systems Inc.

… quantum computers … can solve
problems whose solution will never be
feasible on a conventional computer.
Quantum computing for everyone
Michael Nielsen (2008)
http://michaelnielsen.org/blog/quantum-computing-for-everyone/
Image from http://www.longexposure.ca/2011/03/tedx-waterloo-the-uncharted/4 © Copyright 2013 D-Wave Systems Inc.

II. The machines

Footprint
• ~ 200 square feet
• Closed cycle fridge
• Consumes ~ 15 kW

Processor environment
• 168 lines from room
temperature to processor
• 10 kg of metal at 20
milliKelvin
• 1 nanoTesla in 3D across
processor; 50,000x less than
earth’s magnetic field
2.725 K 8 © Copyright 2013 D-Wave Systems Inc.

What is a quantum computer
•dfd

Fabrication cross-section
© Copyright 2011 D-Wave Systems Inc.
BASE
WIRA
WIRB
WIRC
Resistor
VIA
SiO2
SiO2
WIRD
WIRE
Junction
One of very few processes in the world capable of
fabricating VLSI superconducting processors

The evolution of an idea
Processor evolution overview available at
http://www.youtube.com/watch?v=6VIAL8gQRTI12 © Copyright 2013 D-Wave Systems Inc.
442 qubits
509 qubits

III. What the machines do

Program real numbers on a graph, draw samples

sk  [-1, +1]

sk  [-1, +1]
𝒔 = +𝟏, −𝟏, +𝟏, +𝟏, −𝟏, +𝟏, −𝟏, −𝟏

Program real numbers on a graph
h1
J15

    

N
j
ji
Eji
ijjjN ssJshss
1 ,
1 ,,
h1
J15
sk  [-1, +1]

    

N
j
ji
Eji
ijjjN ssJshss
1 ,
1 ,,
h1
J15
sk  [-1, +1]
Ising model
AKA
QUBO
AKA
Weighted Max2SAT

A simple example
    

3
1
321 ,,
j
ji
ji
ijjj ssJshsss
h J
h1= +1.0 J12= - 1.0
h2= - 1.0 J13= - 0.5
h3= 0 J23= +0.3
h1
h2
h3
J12
J13
J23
s1
s2 s3

s1 s2 s3 
-1 -1 -1 -1.2
-1 -1 +1 -0.8
-1 +1 -1 -1.8
-1 +1 +1 -0.2
+1 -1 -1 3.8
+1 -1 +1 2.2
+1 +1 -1 -0.8
+1 +1 +1 -1.2
Notation: +1 = , -1 = 
    

3
1
321 ,,
j
ji
ji
ijjj ssJshsss

    

N
j
ji
Eji
ijjjN ssJshss
1 ,
1 ,,
h1
J15
sk  [-1, +1]
    Ts
Z
sP /exp
1 


    Ts
Z
sP /exp
1 


Programmable processor devices
Two types: qubits and couplers
s1 s5
h1 h5
J15

Machine language programming
    

N
j
ji
Eji
ijjjN ssJshss
1 ,
1 ,,
Optimization Hardware
Variables sj = ±1 Qubits j
z
Linear terms hj Qubit biases hj
Quadratic terms Jij Coupler biases Jij
Allowed edge set E Locations of couplers between qubits
Objective function values (s1,…,sN) Allowed energy eigenvalues E
1 ,
N
z z z
P j j ij i j
j i j E
H h J  
 
  

Quantum annealing
Start with all qubits in superposition
s3s2 s4s1
s7s6 s8s5
All 28 states equally
likely

Quantum annealing
Turn down superposition, turn up {h, J}

Vesuvius SR10-V6
500 qubits, 1,472 couplers

Duty cycle for Vesuvius
Step 1. Program {h, J} into hardware
• About 50ms
Step 2. Quantum anneal
• About 20 microseconds
Step 3. Measure qubits (draw a sample)
• About 100 microseconds
Step 4. Repeat 2-3 to gather N samples
~ 50 ms for 1 sample, ~ 1.2 s for 10,000 samples

A quantum Boltzmann-like machine
    

N
j
ji
Eji
ijjjN ssJshss
1 ,
1 ,,
h1
J15
sk  [-1, +1]
    Ts
Z
sP /exp
1 


There’s a fascinating hypothesis that a lot
of human perception … can be explained
by a single learning algorithm.
Unsupervised Feature Learning and Deep Learning
Andrew Ng (2011)
http://www.youtube.com/watch?v=I56UugZ_8DI
Image from
http://coursera.com

rose@dwavesys.com

s3s2 s4s1
s7s6 s8s5
s3s2 s4s1
s7s6 s8s5

20130911 idc hpc_geordie_rose_final

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