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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
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
2 © 2018 FUJITSU
Annealing – Thermal, Quantum and Digital
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!
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
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?
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
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
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
9 © 2018 FUJITSU
Quantum
Computers
Neural
Computers
New Compute Architectures
A hardware that can rapidly solve combinatorial
optimization problems
Digital
Annealer
General-purpose Computers
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
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
12 © 2018 FUJITSU
Optimum Route for Robot @ Work
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
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
15 © 2018 FUJITSU
… also for Real Time
 Individualization / lot size 1
 Late orders or changes
 Deviations from the normal
 Optimize in real time
16 © 2018 FUJITSU
Acoustic Optimization by Annealing
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, …)
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
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
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)
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 …
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)
Fujitsu Sans Light – abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ
0123456789 ¬!”£$%^&*()_+-=[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬-
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ýþÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–—―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl
Fujitsu Sans – abcdefghijklmnopqrstuvwxyz 0123456789 ¬!”£$%^&*()_+-=[]{};’#:@~,./<>?|
©¨~¡¢¤¥¦§¨ª«»¬-
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=[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬-
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Quantum Algorithms @ work - Short introduction to Quantum Annealing and operative applications

  • 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)
  • 24. Fujitsu Sans Light – abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789 ¬!”£$%^&*()_+-=[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬- ®¯°±²³µ¶·¸¹º¼½¾¿ÀÁÂÃÄÅÇÈÆÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúûü ýþÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–—―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl Fujitsu Sans – abcdefghijklmnopqrstuvwxyz 0123456789 ¬!”£$%^&*()_+-=[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬- ®¯°±²³µ¶·¸¹º¼½¾¿ÀÁÂÃÄÅÇÈÆÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúû üýþÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–—―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl Fujitsu Sans Medium – abcdefghijklmnopqrstuvwxyz 0123456789 ¬!”£$%^&*()_+- =[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬- ®¯°±²³µ¶·¸¹º¼½¾¿ÀÁÂÃÄÅÇÈÆÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùú ûüýþÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–— ―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl

Editor's Notes

  1. Need a lot more words. Put the parts in to the box and shake it up to make it more disordered. When it fall down
  2. Need a lot more words. Put the parts in to the box and shake it up to make it more disordered. When it fall down
  3. CPU performance improvement will becomes slow. For example, Big CPU frequency improvement is not expected already. However, customers still have a lot of problems they want to solve but cannot be solved by current general purpose computers. So, New computing architecture is required. This is current ICT trend. Quantum computers are expected to be a major candidate of a new computing architecture. However, we think they are still under R&D phase. There are still many challenges for real business problems, such as scale, stability. Digital Annealer is a new technology to solve large-scale combinatorial optimization problems instantly. It uses a digital circuit design inspired by quantum phenomena and can solve problems which are tough for current general purpose computers to deal with.
  4. Top finding – speed – Combinatory Optimization problem Use case briefing Availability details.. What we don’t know…What we need to know for planning purpose Proposed next steps
  5. This is the DA road map. Yesterday is initiation of service by using 1st generation. Fujitsu plans to release 2nd generation in this fiscal year. The we expand scale up to 8K bits and precision up to 64bits. This is the chip. In next fiscal year, as a next generation, Fujitsu develop large scale parallel processing.
  6. Spiegel Use Case  Gesucht ist die optimale Oberflächen-Umformung des Spiegels, sodas das Fargeräusch minimal ist Der Use Case wurde mit Digital und Quantum Annealer getestet  Die Ergebnisse sind vergleichbar Der digital Annealer hat einen besonderen Chip mit 1024 klassischen Bits (damit werden keine Qbits emuliert!) Bei dem digital Annealer funktioniert das Tunneling mithilfe eines Tricks
  7. Spiegel Use Case  Gesucht ist die optimale Oberflächen-Umformung des Spiegels, sodas das Fargeräusch minimal ist Der Use Case wurde mit Digital und Quantum Annealer getestet  Die Ergebnisse sind vergleichbar Der digital Annealer hat einen besonderen Chip mit 1024 klassischen Bits (damit werden keine Qbits emuliert!) Bei dem digital Annealer funktioniert das Tunneling mithilfe eines Tricks
  8. Der Wind erzeugt indirect Geräusche, indem er den Spiegel trifft und auf das Fahrzeug reflektiert wird  Diese Refklektionen erzeugen bei dem Aufprall Schallwellen Optimierungsproblem  möglichst wenige / keine Reflektionen auf das Fahrzeug Es sind unendlich viele Kombinationen möglich, die simuliert werden müssen, um das Problem optimal zu lösen  Sehr hohe Komplexität durch Anzahl möglicher Kombinationen
  9. Akustisches Monopol = Spiegel ???? Reflektierende Oberfläche = Scheibe ???? Mikrofon = Ohr des Fahrers  hier sollen möglichst wenige Reflektionen ankommen
  10. Mic received energy = Wie viele Schallwellen kommen an?