20130912_mit _geordie_rose

The D-Wave Two
Dr. Geordie Rose
Founder and CTO, D-Wave
High Noon September 12th 2013
@ MIT

Image from
http://www.nas.nasa.gov/quantum/quantumcomp.html

What I’m going to talk about
1.
2.
3.

What we are doing (& why)
The machines
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

3


Image from
http://conferences.ted.com/TEDGlobal2010/program/

Mungo Park … sailed from Portsmouth on the
30th of January, 1805, resolved to trace the
Niger (River) to its source or perish in the
attempt. He perished.
Introduction to Travels in the Interior Districts of Africa 1795-7
Mungo Park (1799)
http://ebooks.adelaide.edu.au/p/park/mungo/travels/complete.html

4


… 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/

5


Image from http://www.longexposure.ca/2011/03/tedx-waterloo-the-uncharted/

Someday, perhaps soon, we will build a
machine that will be able to perform the
functions of a human mind, a thinking
machine.
The Connection Machine
Danny Hillis (1985)

6

Image from http://compass-summit.com/danny-hillis/

II. The machines

7


8


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

9


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

10


11


What is a quantum computer

• dfd

12


Fabrication cross-section
WIRE
WIRD

SiO2

WIRC
WIRB

SiO2
VIA

Resistor

Junction

WIRA

BASE

One of very few processes in the world capable of
fabricating VLSI superconducting processors
© Copyright 2011 D-Wave SystemsD-Wave Systems Inc.
13
© Copyright 2013 Inc.

Qubits
Compound compound Josephson junction RF-SQUID
niobium flux qubits
Control the bias towards
one or the other state –
‘sigma Z’

Control the amount of superposition –
‘sigma X’

Processor architecture overview available at
http://www.youtube.com/watch?v=PqSgmCg1kew

Qubits are stretched into long thin
loops and coupled together

15


Unit cells tile the plane

=
16


A physical ‘computing fabric’

17


The evolution of an idea

509 qubits
442 qubits

18


Processor evolution overview available at
http://www.youtube.com/watch?v=6VIAL8gQRTI

III. What the machines do

19


Program real numbers on a graph, draw samples

20



sk  [-1, +1]

21



sk  [-1, +1]

22


Program real numbers on a graph

h1

J15
23


  s1 , , s N  

N

h

j

sj 

j 1

J

ij

si s j

i , j E

sk  [-1, +1]

h1

J15
24


  s1 , , s N  

Ising model

N



hjs j 

j 1



J ij s i s j

i , j E

AKA

QUBO
AKA

sk  [-1, +1]

Weighted Max2SAT

h1

J15
25


A simple example
  s1 , s 2 , s 3  

3

h

j

sj 

j 1

J

ij

si s j

i j

h1
s1

J12
s2

h

J23

s3

h2
26

h1= +1.0
h2= - 1.0
h3= 0

J13


h3

J
J12= - 1.0
J13= - 0.5
J23= +0.3

  s1 , s 2 , s 3  

3

h
j 1

j

sj 

J

ij

si s j

i j

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 = 
27


  s1 , , s N  

N

h

j

sj 

j 1

J

ij

si s j

i , j E

sk  [-1, +1]

h1

J15
28




1
P s  
exp    s  / T 
Z



1
P s  
exp    s  / T 
Z

29


Programmable processor devices
Two types: qubits and couplers

h1
30

s1


s5

J15

h5

Machine language programming
N

N

  s1 , , s N    h j s j  
j 1

J ij s i s j

HP 

i , j E



h j j 

j 1

z



J ij i 
z

z
j

i , j E

Optimization

Hardware

Variables sj = ±1

Qubits jz

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

31


Quantum annealing
Start with all qubits in superposition

s1

s3

s4

s5

32

s2

s6

s7

s8


All 28 states equally
likely

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

33


Vesuvius SR10-V6
500 qubits, 1,472 couplers

34


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
35


A quantum Boltzmann-like machine
  s1 , , s N  

N

h

j

sj 

j 1

J

ij

si s j

i , j E

sk  [-1, +1]

h1

J15
36




1
P s  
exp    s  / T 
Z

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

37


Image from
http://coursera.com

rose@dwavesys.com

38


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