20130911 idc hpc_geordie_rose_final

transcript

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

Quantum computation … will be the first technology that allows useful tasks to be

performed in collaboration between parallel universes.

David Deutsch @ TED 2005

… 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/

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

What is a quantum computer

•dfd

Fabrication cross-section

Resistor

SiO2 WIRD

Junction

One of very few processes in the world capable of fabricating VLSI superconducting processors

The evolution of an idea

442 qubits

509 qubits

III. What the machines do

Program real numbers on a graph, draw samples

sk [-1, +1]

𝒔 = +𝟏, −𝟏, +𝟏, +𝟏, −𝟏, +𝟏, −𝟏, −𝟏

Program real numbers on a graph

ijjjN ssJshss1 ,

sk [-1, +1]

ijjjN ssJshss1 ,

sk [-1, +1]

Ising model AKA

QUBO AKA

Weighted Max2SAT

A simple example

321 ,,j

ijjj ssJshsss

h J h1= +1.0 J12= - 1.0

h2= - 1.0 J13= - 0.5

h3= 0 J23= +0.3

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 =

321 ,,j

ijjj ssJshsss

ijjjN ssJshss1 ,

sk [-1, +1]

sP /exp1

Programmable processor devices Two types: qubits and couplers

Machine language programming

ijjjN ssJshss1 ,

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

Nz z z

P j j ij i j

j i j E

Quantum annealing Start with all qubits in superposition

s3 s2 s4 s1

s7 s6 s8 s5

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 Program real numbers on a graph, draw samples

ijjjN ssJshss1 ,

sk [-1, +1]

sP /exp1

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

s3 s2 s4 s1

s7 s6 s8 s5

s3 s2 s4 s1

s7 s6 s8 s5

20130911 idc hpc_geordie_rose_final

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