d-wavequantum computingaccess & applications via cloud deployment

Dr. Colin P. WilliamsVice President of Strategy & Corporate DevelopmentD-Wave Systems Inc.

D-Wave Quantum ComputingAccess & applications via cloud deployment

2Copyright © D-Wave Systems Inc.

D-Wave’s Mission & Activities

• Mission– To solve the world’s hardest problems especially in the

areas of artificial intelligence and machine learning

• Core technologies– Superconducting annealing-based quantum computers– Hybrid quantum/classical algorithms & architectures

• Business model– Quantum computer system sales– Quantum computer cloud services– Quantum machine learning services


What are Quantum Computers?


What are Quantum Computers?

Computers that harness quantum physical effects not available to conventional computers

Quantum Processor


Superposition

Entanglement

Quantum Tunneling

Which Quantum Effects are Used?


Our Approach in Context

• Annealing (D-Wave, Google, IARPA)– Harnesses Nature’s ability to find low energy configurations via quantum tunneling– Resilient to noise / does not require long coherence times / MIT pedigree– Handles a wide range of important problems– Currently non-universal but could be made universal

• Gate Model (Google, IBM, Intel, Alibaba, Rigetti)– Most common approach / based on analogy with Boolean logic circuits– Very difficult to scale; requires massive qubit overhead for error correction

• Topological (Microsoft)– Like gate model but without need for error correction (in theory)– Needs exotic quasi-particle whose robustness is now in dispute

(Phys. Rev. Lett., 118, 046801, 26 Jan 2017)


D-Wave’sQuantum Computer


Current Product: D-Wave 2000QTM


World’s Most Advanced Quantum Processor


Superconducting yet made in a CMOS Foundry

$1B World Class Production Facility 2000-qubit Circuits at 129,000 JJs .25μm design rules ASML 193nm lithography 65nm


Functional Quantum Computation Established• Papers show superposition, entanglement & co-tunneling

– Johnson et al., “Q. Annealing with Manufactured Spins,” Nature 473, 194-198, 12th May (2011).– T. Lanting et al., “Cotunneling in pairs of coupled flux qubits,” Phys. Rev. B 82, 060512(R) (2010).– T. Lanting et al., “Entanglement in a Q. Annealing Processor,” Phys. Rev. X 4, 021041 (2014).

• These quantum effects play a functional role in the computations– Boixo, et al., "Computational multiqubit tunneling in programmable quantum annealers," Nature Communications 7,

Article number: 10327, Published 07 January (2016).

• UCL/USC showed that none of the classical models so far proposed as explanations for the D-Wave machine are correct– Albash et al., “Consistency Tests of Classical and Quantum Models for a Quantum Annealer,” Phys. Rev. A 91, 042314,

Published 13 April (2015).

• USC & D-Wave showed q. annealing can occur successfully on timescales orders of magnitude longer than the coherence time– Albash et al., "Decoherence in adiabatic quantum computation," Phys. Rev. A 91, 062320 (2015).– N G Dickson et al. “Thermally assisted quantum annealing of a 16-qubit problem”, Nature Communications 4, Article

number: 1903, 21 May (2013).


Is Quantum Computing Ready for Deployment?

http://www.fz-juelich.de/ias/jsc/EN/Research/ModellingSimulation/QIP/QTRL/_node.html

JülichSupercomputing Center created a framework for Quantum Technology Readiness Levels

D-Wave

quantum

annealer

IBM

Google

Experimental

qubit devices


The Potential


Potential for Massive Speedups

Copyright © 2017 D-Wave Systems Inc. 14

• Google found D-Wave 2X was 100,000,000x faster than QMC and SA on a particular problem (their “quantumess” test)

• More competitive classical algorithms become ineffective as we move to higher connectivity chips

See “What is the Computational Value of Finite Range Tunneling?” arXiv:1512.02206v3

• Apparent parallel scaling of QMC & D-Wave is an artifact (see E. Andriyash et al. “Can QMC simulate QA?” arXiv:1703.09277)


Potential for Better Performance vs. Power Scaling

Source: http://www.extremetech.com/computing/116561-the-death-of-cpu-scaling-from-one-core-to-many-and-why-were-still-stuck

Quantum Power, ~0.1 μW

Classical Power

ClassicalPerformance

Quantum Performance

See: http://www.dwavesys.com/sites/default/files/14-1005A_D_wp_Computational_Power_Consumption_and_Speedup.pdf

• Transistors (000s)• Clock Speed (MHz)• Power (W)• Perf/Clock (ILP)

Rainier

Vesuvius

Washington


Potential for Faster & Better (Lower Energy) SamplingEn

ergi

es o

f Sa

mp

les

Ret

urn

ed

Quantum ClassicalFat Tree #1

ClassicalFat Tree #2

Classical Selby

ClassicalSA #1

ClassicalSA #2

Best sample so far


Quantum Sampling Accelerates Learning

• Specify model parameters θtrue, draw exact Boltzmann samples from θtrue, and estimate θ from samples

• Compare efficacy of CD, PCD, and QA-seeded MCMC chains at estimating the true distribution

• Compare rate of learning of a fully visible probabilistic graphical model classically vs. quantumly

Goal Model to Learn

Procedure

Classical Learning via PCDClassical Learning via CD

Quantum Learning (learns true θ faster)

D. Korenkevych et al., “Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines,” arXiv:1611.04528

Result: Quantum Learns Faster


Factoring

Finding Ramsey

Numbers

Diagnosis

Constraint Satisfaction

Monte Carlo

FinancialModeling

SAT Filters

Discrete Sampling

Complexity Scaling

Quantum ErrorCorrecting Codes

QuantumSimulation

Quantum Research

Discrete Optimization

RadiotherapyOptimization

Deep Learning

VariationalAutoencoders

BoltzmannMachines

Applications Customers Have Mapped to D-Wave

Optimization Sampling Science

Structured Prediction

Trading Trajectory

OptimizationDetecting

Market Instability


Lowest Level Programming

Computational Problem to Solve

Equivalent Ising Problem

Run Quantum Annealing

Read Out a Spin-configuration having Low Energy

Embedded Ising Problem

Retain all Solutions (SAMPLING)

Retain Best Solution(OPTIMIZATION)

Store Spin Configuration & Repeat


Programming Languages & Software Tools

• Programming Languages– Python, Matlab, C/C++

• Compilers– Map problem to Ising problem

• Embedders– Ising problem to processor graph

• Pre-processing– Determining any forced variables– Decomposing larger problems

• Post-processing (optional)– Seeding heuristic optimizers– Inline GPU & CPU computations


Applicationsin Finance


PROBLEM: Invest $K amongst N assets at T time steps so as to maximize expected returns subject to varying risk and transaction costs at each time step

APPROACH: Quantum Optimization via D-Wave

• Couch problem as a quadratic integer optimization problem

• Map integer constraints to QUBOs

• Minimize sum of QUBOs via quantum annealing

Returnsat each time step

Transaction Costs

Sum of holdings at each time step = K

Max allowed holding of each asset = K’

Risk: Σ = forecast covariance matrix; γ = risk aversion

Optimization: Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer arXiv1508.06182

IMPACT:Finds optimal strategy subjectto realisticconstraints

G. Roseberg et al (1QBit), M. L. de Prado (Guggenheim Partners), P. Carr (Courant Inst.) & K. Wu (LBL)


PROBLEM: Seek signature of impending market instability by detecting onset of anomalously correlated moves

APPROACH: Model market as a graph; nodes = assets; edge if correlation > c

• Continually re-compute largest clique / Sudden expansion in clique size signals market move

Optimization: Impending Market Instability

IMPACT: Signals imminent market instability


Application inHealthcare


• Represent a model as a bit string

• HPC: bit string -> model -> simulation -> evaluation -> “score”

• D-Wave: Learn from results, adjust the program QC runs & iterate

• So QC guesses solution / HPC scores it / Adjust QC & repeat

Quantum-Accelerated Optimization

Better “guesses”

“Scores” for the guesses

Use QC to reduce number of calls to HPC


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

Classical

Dis

crep

ancy

Design Parameter

Quantum

Dis

crep

ancy

Design Parameter


Case Study: Radiotherapy Optimization

PROBLEM: Deliver lethal dose to tumor whilst minimizing damage to healthy tissues

APPROACH: Hybrid: QC + Conventional Computer• Radiation treatment plan = bit string• Quality = result of running extensive

radiation transport simulation• Results of radiation transport simulations

drive adjustments to plan

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 sim. Source: “Varian’s RapidArc Radiation Delivery

System Goes Clinical,” medGadget, July 22nd (2008)


Applications inMachine Learning


People Have Used D-Wave for Machine Learning

• Mainly supervised learning so far– Yes/No classifier for cars in images

– Wink/Blink classifier

– Yes/No Higgs boson event classifier

Source: A. Mott, J. Job, J. Vlimant, D. Lidar & M. Spiropulu, Nature, Volume 550, 19th October 2017.


What Current A.I. Doesn’t Do Well

“Unsupervised learning had a catalytic effect in reviving interest indeep learning, but has since been overshadowed by the successes ofpurely supervised learning. […] we expect unsupervised learning tobecome far more important in the longer term. Human and animallearning is largely unsupervised: we discover the structure of theworld by observing it, not by being told the name of every object.”

Yann LeCun, Yoshua Bengio & Geoffrey Hinton,“Deep Learning,” Nature, Vol. 521, 28th May (2015)

• A.I. is not yet sufficiently good at unsupervised learning


Quantum Computers Can Help

• Unsupervised learning can use probabilistic models

• These rely on sampling

• Quantum computers have potential to revolutionize A.I. by making unsupervised learning models feasible to train efficiently

• Because quantum computers are fast native samplers


How does Sampling Arise in Probabilistic Models?

• Consider a Restricted Boltzmann Machine (RBM)

• RBMs can be components of more complex neural networks

Model parameters q º {bi}È{c j}È{Wi j}

Visible units with biases bi

Hidden units with biases cj

W1,1W1,2

W10,10


What does Training Entail? Discrete Sampling!

• Given training data (visible vectors) vt s.t. 1 ≤ t ≤ T

• Adjust model parameters s.t. model most likely reproduces the training data

• Done by maximizing the log-likelihood of the observed data distribution w.r.t. the model parameters, θi

¶

¶qilog(p(vt ))

t=1

T

åæ

è

çç

ö

ø

÷÷= -

¶

¶qiE(vt,h)

t=1

T

åp(h |v

t)

+T¶

¶qiE(v, h)

p(v, h)

• Positive Phase

• Expectation over p(h|vt) in “clamped” condition

• Requires sampling over the (given) data distribution

• Simple!

• Negative Phase

• Expectation over p(v, h) in “unclamped” condition

• Requires sampling over the (predicted) model distribution

• Intractable!

where


ooooDirected hierarchical

aanetwork of continuousvariables

Directed hierarchical

aanetwork of continuousoooovariables

Discrete Sampling in Complex Architectures (DVAE/QVAE)

• Real data has discrete & continuous variables

• Natural to want discrete hidden variables

• Can’t backpropagate through discrete variables

• DVAE solves this problem– See J. Rolfe, “Discrete Variational Autoencoders”,

arXiv:1609.02200 Undirected network of discrete variables (a fully hidden bipartite (possibly quantum) Boltzmann machine

Encoder Network

Decoder Network

Sample from smoothed distribution

Smoothed (i.e., continuous) discrete latent variables

Random samples from U[0,1]

• Exceeds state of the art on three standard machine learning datasets

• DVAE (classical) / QVAE (quantum)


Training Digits

DVAE

Previous State of the Art

J. Rolfe, “Discrete Variational Autoencoders”, arXiv:1609.02200 [stat.ML]

DVAE Exceeds State-of-the-Art on a Generative Task

Machine-Generated Novel DigitsReconstructed Digits


Why we Believe QVAE will Further Improve DVAE

State-of-the-Art (classical) DVAE (classical) QVAE (via QMC simulation)

Same number of training epochs used in each case


Quantum/Classical Machine Learning Services

• D-Wave web services are designed to make it easier to train PML models

• Capabilities– Learns from noisy / incomplete data– Quantifies confidence in predictions– Reveals hidden correlations in data– Infers missing data

• Functionality (Web Services for PML):– Classical Boltzmann sampling (GPU)– Quantum Boltzmann sampling

(CPU)– Raw QPU sampling (QPU)

• Supports both ML/QML models

• Called from TensorFlow or Python


HybridQuantum/Classical

Cloud Model


Quantum Cloud

• Quantum computers …– Need typical datacenter infrastructure

– Are expensive

– Are evolving fast / quickly obsolete

– Are alien to most programmers

• Solution– Make QCs accessible via the cloud

– Bill usage by minute or by time blocks

– Lease QC time rather than buy QC

– Access via familiar software & tools

– Includes classical proxy solvers for code development & debugging


Bill Gates Expects Quantum Cloud Access [by 2026]

Source: http://www.zdnet.com/article/quantum-cloud-computing-could-arrive-in-the-next-decade-says-bill-gates/


D-Wave Sells Quantum Cloud Access Already

Jane Edwards, "D-Wave to Provide Oak Ridge National Lab Cloud Access to Quantum Computing", ExecutiveBiz.com blog post, July 26th, (2017) web URLhttp://blog.executivebiz.com/2017/07/d-wave-to-provide-oak-ridge-national-lab-cloud-access-to-quantum-computing-platform/

http://blog.executivebiz.com/2017/07/d-wave-to-provide-oak-ridge-national-lab-cloud-access-to-quantum-computing-platform/


Conclusions

• Quantum computing will turbo-charge unsupervised learning

• Quantum and hybrid machine learning models already running

• Our first web-ML services were released in September 2017

– Reinvigorate probabilistic machine learning and prepare ground for future quantum & hybrid ML services

– Both state-of-the-art today (classically) / faster tomorrow (quantumly)

• DVAE already surpassing state of the art / QVAE coming in 2018

• Seeking users for our quantum cloud services!

Contact: [email protected]


Thank you!Email: [email protected]