This document provides an overview of quantum annealing and its applications. Quantum annealing can be used to find the optimal solution to complex optimization problems much faster than classical computers. It does this by acting on problems simultaneously rather than sequentially. The Digital Annealer, a quantum-inspired annealing machine, can currently solve certain problems faster than traditional simulated annealing on classical hardware. Potential applications discussed include logistics routing, portfolio optimization, molecular modeling, and acoustic design optimization.

1. Human Centric Innovation
Co-creation
for Success
© 2018 FUJITSU
Human Centric Innovation
Co-creation
for Success
Quantum
Algorithms
@ Work
Short introduction to
Quantum Annealing and
operative applications.
München, 22.11.2018
Dr. Fritz Schinkel
Industrial Analytics & Quantum Computing
Industry 4.0 Competence Center

2. 1 © 2018 FUJITSU
Why look beyond classical computing?
Logistics / Traffic route optimization
Continuous redistribution of position data
 Identifying optimal route
 Parallel fleet routing
 Warehouse routing
Disaster Recovery
Complex stochastic problem that combines:
 Time-critical optimization
 Resource allocation
Finance and Economic Policy
Dealing with 1036 combinations or more
 Portfolio risk optimization
 Credit risk scoring
 ATM - Cash replenishment
Chemical & Material Science
Complex molecule structures:
 Molecular similarity
 Lock and key model
 Protein folding
Cryptography
Cybersecurity use cases
 Factorization breaks keys
 Security protocols
 Unbreakable Encryption
Design and Construction
Finite element structure
optimization
 Acoustic design
 Fluid simulations
 Crash simulation

3. 2 © 2018 FUJITSU
Annealing – Thermal, Quantum and Digital

4. 3 © 2018 FUJITSU
15 pieces in 3 minutes
20 pieces in 10 years
30 pieces in
1.100.000.000.000.000years
(appr. 100.000 time the age of the universe)
Assume: 1 combination takes 1 second
Then mankind (~7.5 B) could solve …
Combinatorics by combination: Life is too short!

5. 4 © 2018 FUJITSU
Such problems require new thinking
 CPU (sequential):
Operations in the same place at
different time
 GPU / HPC (parallel):
Operations in different place at
the same time
 Quantum (simultaneous)
Operations in the same place at
the same time complexity
time
sequential
parallel
simultaneous

6. 5 © 2018 FUJITSU
 Not annealing:
 Run by trial and error through
all combinations
 Huge number of steps (“n!”)
 Annealing:
 Shake heavily & let fall
 Shake less & let fall ...
 Stable solution after “few” rounds
 Annealing of sword:
 Shake = heat, fall = hammer
 stable = durable
What is Annealing?

7. 6 © 2018 FUJITSU
Alternative: Simulated Annealing
(Algorithm on classical hardware)
 Find minimal (or maximal) value of function
(e.g. disorder, cost, energy)
 Algorithm:
1. Move randomly to lower energy
2. Energy reduction is always accepted
3. Small energy increase possible due to
thermal noise but less probable when system
cools down
4. End in (local) minimum
5. Reheat / repeat to improve
Energy
Search space (state)
1
2
3
4
…5

8. 7 © 2018 FUJITSU
From Simulated to Quantum Annealing
 Classical system:
 Gets stuck to local minimum
 Needs reheating
 Does not know “left or right”
 Statistically prefers wider not deeper valleys
state
E
?
 Quantum system:
 Can’t be locked by barriers
 Will tunnel into deeper valley
 Is more attracted by deepest valley
 Finds global minimum !
state
E

9. 8 © 2018 FUJITSU
Available today from
 First commercial quantum
computer
 3x3x3m cube for magnetic
shield and 10mK cooling
system
 25kW power consumption
 2048 qubits
 Programming by selecting
connected qubits

10. 9 © 2018 FUJITSU
Quantum
Computers
Neural
Computers
New Compute Architectures
A hardware that can rapidly solve combinatorial
optimization problems
Digital
Annealer
General-purpose Computers

11. 10 © 2018 FUJITSU
Quantum Inspired: FUJITSU Digital AnnealerDifferentiators
• Full connectivity through the 1024
bit scale with 16-bit precision
• Provides the ability to represent a
large scale problem effectively
Easy Problem Mapping
• Parallel processing making it
much faster than standard
computing
• Stochastic parallelism providing
significant speed up
Parallel Speed up
Uniquearchitecture
Annealing Process
• DA increases escape probability
from the local minimum energy state
with the hardware offsetting
• Faster than traditional simulated
annealing

12. 11 © 2018 FUJITSU
2018 2019
1st Generation
2nd Generation
1,024 bit
16 bit precision
(with 65,536
gradient values) in
bit interconnections
Up to 8,192 bit
Up to 64 bit precision (18.45
quintillion gradations) in bit
interconnections
DAU：
Digital Annealing Unit
Cloud
+ On-Premises
Next Generation
Large scale
parallel-
processing
1 million bit
scale
Digital Annealer Road Map
Roadmap are subject to change without notice
Cloud Service

13. 12 © 2018 FUJITSU
Optimum Route for Robot @ Work

14. 13 © 2018 FUJITSU
Robots are not traveling salesmen
 Robots “visit” PVC sealing seam locations
 Seam have physical extension and can
be sprayed in 2 directions
 Goal for optimized working instructions
is to find …
 … best distribution of work between robots
 … best spraying directions and optimal
roundtrip between endpoints

15. 14 © 2018 FUJITSU
Free Path Optimization
 Physical model
 Set of spraying positions for seams
 Distance / time between seam endpoints
 Goal
 Fastest execution of work
 Find best visiting order
 Annealing model
 Bits: Entering seam at certain time and
ending
 Energy: Length / time of free path length

16. 15 © 2018 FUJITSU
… also for Real Time
 Individualization / lot size 1
 Late orders or changes
 Deviations from the normal
 Optimize in real time

17. 16 © 2018 FUJITSU
Acoustic Optimization by Annealing

18. 17 © 2018 FUJITSU
External Mirrors and the Sound of Silence
 Airflow around moving vehicle creates sound.
 External mirror’s shape has significant
influence on noise.
 Goal is reduced noise emission of reshaped
mirror which …
 … minimizes the amount of noise perceived by
the driver
 … still retains required functional properties of a
mirror (size, stability, …)

19. 18 © 2018 FUJITSU
surface
Annealing Model for Noise Minimization
 Physical model
 Acoustic monopole
 Reflecting surface
 Sensitive microphone
 Goal
 Minimal reflection to microphone
 Find best surface deformation
 Annealing model
 Bits: Alternative positions for each corner
 Energy: Sound reflected to microphone
source microphone
Positions
corner A Positions
corner B

20. 19 © 2018 FUJITSU
Experiment: Annealing in a loop
Initial surface: Sphere
Triangulated shape with
alternative corner shifts
Annealing:
Finds best new shape
mic impact is zero?
done
yes
no

21. 20 © 2018 FUJITSU
Results / Conclusions
 Sphere triangulation with 122 nodes
and 8 alternatives per node.
 Zero impact after 28 iterations in less
than 4 minutes (Digital Annealer V1)
 Stable convergence to final solution
 Conceptual approach fits for
 Other shapes
 Other target functions
 Finite element problems in general
Annealing energy Mic received energy (rel. solid angle)

22. 21 © 2018 FUJITSU
Summary / Value Proposition
 Caveat: Quantum computing is not a general purpose computer
 Quantum annealing solves optimization problem disruptively fast
 Digital Annealer makes QC algorithms practically applicable today:
 more logical Q-Bits
 much higher precision
 much less complexity and power consumption
 Leverage Fujitsu “Proof of Concept” consulting and
develop and apply tomorrow algorithms today
And …

23. 22 © 2018 FUJITSU
… one more thing to show : DAU
Lev Davidovich Landau
aka “DAU”
Soviet Physicist
(1908-1968)
Dr.Joseph Reger
CTO Fujitsu EMEIA
(Fujitsu Forum 2017)
DAU in the
exhibition
(DIGICON 2018)

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