D-Wave Systems Podcast

Quantum Computing:
A New Resource for HPC
Colin P. Williams
D-Wave Systems Inc.

What I’ll Talk About
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D-Wave Systems Inc.
What are quantum computers?
D-Wave’s approach & rationale for it
Our current product: The D-Wave TwoTM Quantum Computer

• Integrating QC into HPC

© 2013 D-Wave Systems Inc. All Rights Reserved

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D-Wave Systems Inc.
• Privately owned based in Vancouver, Palo Alto & Washington D.C.
– Investors: DFJ, Goldman Sachs, In-Q-Tel, Jeff Bezos, et al

• Offering

quantum computing systems

– Built from superconducting processors

• ~100 employees (27Ph.D., 18 B.Eng. 11 M.Sc.)
• 100+ patents / 60+ peer reviewed papers

• Unique infrastructure (design, fab, test, systems, software)


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Ranked 4th in Patent Power for Computer Systems


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Working with Leaders
Defense

Universities


Intelligence

Web

Finance

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National Labs

Energy

What are Quantum
Computers?


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What are Quantum Computers?

• Computers that harness quantum physical
phenomena not available to conventional
computers, e.g.,
– Superposition
– Entanglement
– Co-tunneling
– Non-determinism


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Why are they Interesting?
• Pragmatically
– Allows us to solve problems in new ways to beat the best
we can do classically in many cases

– Exponential speedups
• Factoring
• Simulating quantum systems
• Quantum chemistry

– Polynomial speedups
• Unstructured search
• Structured search (NP-complete & NP-hard problems)

• Philosophically
– As Prof. David Deutsch of Oxford University says …

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“Quantum computation … will be the first
technology that allows useful tasks to be
performed in collaboration between
parallel universes”
Prof. David Deutsch – The Fabric of Reality


PAGE
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Significance to HPC
of harnessing quantum mechanics
Metric

High Performance Computer

Quantum Computer
One 512-qubit core has ~10154 “virtual
threads” in superposition, but quantum
mechanics limits our ability to read them

Concurrency

108 cores & 1010 threads

Robustness

Reduced operating voltages & channel Naturally probabilistic programming.
widths, will make devices less reliable.
Quantum annealing degrades gracefully to
Need new programming style that is
errors
intrinsically probabilistic and tolerant to
errors

Power

Expect 25-100MW systems. Few
locations can support this demand.
Fewer data centers can afford it. Power
demand dominated by data movement

15kW for cooling & ~0kW for computation.
Cooling power will stay constant up to
thousands of qubits! Almost no energy to
compute. No data movement needed

Storage

Needs to be 100PB capacity but will be
constrained by physical & economic
limits (density, power, cost)

Memory exploits parallel universes. Create &
process superposition of all 2N configurations
at once. N > 300 qubits provide more storage
than there are particles in the known Universe

Speed

1018 FLOPS

Potential to be fast but runs at 0 FLOPS


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Will QCs Make HPCs Obsolete?
• Probably not . . .

• They’re suited to different tasks
– HPCs: Computational fluid dynamics, molecular
simulation, weather forecasting, nuclear weapons
modeling, etc.

– QCs: discrete combinatorial optimization, artificial
intelligence, machine learning, sampling

• But together they can enhance each other


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D-Wave’s Approach
Quantum Annealing
inspired by Adiabatic Quantum Computing


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How Quantum Annealing Works
• Space of solutions
defines and energy
landscape & best
solution is lowest valley
• Classical algorithms can
only walk over this
landscape
• Quantum annealing can
tunnel through the
landscape

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Our Product


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D-Wave TwoTM Quantum Computer


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Cooling
• Closed cycle dilution
refrigerator (“fridge”)
• Fridge + servers
consume 15.5kW
• Power demand will
remain constant as we
scale up to thousands of
qubits


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Processor Environment
• 192 lines from room
temperature to chip
• 10kg cooled to 0.02K
• 150x colder than
interstellar space
• Shielded room excludes
external RF

• Magnetic field < 10−9 Tesla
across chip
• 50,000x weaker than
Earth’s field

• Isolated from vibrations

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Product Roadmap
2,048-qubits
Q4 2015

D-Wave One

D-Wave Two

Talk of “the” D-Wave chip is a misnomer – the architecture evolves 6-8 times per year

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512-Qubit Vesuvius Processor


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Processor Architecture
Physical Layout

Physical Unit Cell

Wiring layout
• 8 8 array of 8-qubit unit cells
• Within each unit cell each
vertical qubit is coupled to
each horizontal qubit
• Vertical (horizontal) qubit
coupled to corresponding
qubit in vertical (horizontal)
neighboring cells

Logical Layout

Logical Unit Cell

q1
q2

q5
q6

q3

q7

q4

q8

Topology of interconnect network

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• Non-planarity of interconnect
network makes the problem of
finding the lowest energy state
of the qubits NP-hard
• NP-Hardness guarantees you
can map many practical
problems to the architecture

Programming Languages
• User does not need to know anything
about quantum physics
– Just pass a matrix of hi’s and Jij’s to the machine

• Currently have interfaces for:
– Python
– Matlab
– C/C++


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Integrating QC into HPC


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A Strategy for Integrating QC with HPC
• HPCs excel at large scale numerical simulations
• QCs excel at discrete combinatorial optimization
• Can we use a HPC + QC for engineering design optimization?
• Problem setup …
– Suppose engineering design is specified by a bit string
– Various designs can be “scored” by running some HPC simulation
– Goal is to find the bit string whose score meets design criteria


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Division of Labor
• HPC’s job
– Map bit string to design; simulate design; score the result; send score to QC

• QC’s job
– use sequence of bit string/score pairs to tweak the h’s and J’s in the QMI so
that it will yield samples that correctly mimic the ordering of energies of
solutions in the neighborhood of the highest scoring bit string
– Yields a new QMI
– Run new QMI many times to yield new candidates (~104 solutions/sec)
– HPC scores the candidate solutions and returns scores to QC
– Iterate until design meets desired criteria


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Quantum-Accelerated HPC
• Using the QC + HPC together allows faster convergence on
optimal design than is attainable by using HPC alone
• Avoids unnecessary HPC cycles & power consumption
– Increases availability of HPC for running other computations
– Works best when cost of running the HPC simulator is high

• Supercomputer + quantum computer
Better “guesses”
“Scores” for the guesses

Quantum-Accelerated HPC

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Why Does it Work?
• Imagine you’ve reached an intermediate point in design space
and want to pick the next bit string to try
• Classical methods only sense the local neighborhood
• Quantum methods have potential for greater horizon
• Make a better next move possibly leading in different direction

Quantum
Discrepancy

Discrepancy

Classical

Design Parameter

Design Parameter
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Example
Radiotherapy Optimization


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Another Example: Radiotherapy Optimization
PROBLEM:

Deliver lethal dose to tumor whilst
minimizing damage to healthy
tissues

APPROACH:

Hybrid: QC + Conventional Computer
• Design = bit string
• Quality = result of running extensive
radiation transport simulation
• D-Wave system learns from
simulations to predict better designs

IMPACT:
• Hybrid quantum-classical design found a radiation
therapy treatment that minimized the objective
function to 70.7 c.f. 120.0 for tabu, and ran in 1/3
the time making fewer calls to radiation transport
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simulation

Conclusions
• We see quantum computing as a new
resource for HPC
• Technology scaling faster than Moore’s Law
• 1,024-qubit quantum computer by mid-2014

• Performance is encouraging
• Many potential uses in combinatorial optimization, engineering

design optimization, A.I., machine learning & sampling
• Seeking early adopters to explore QC-HPC synergy

Email: cpwilliams@dwavesys.com

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Thank you!
Email: cpwilliams@dwavesys.com


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D-Wave Systems Podcast

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D-Wave Systems Podcast