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From Acoustic Design to Shop Floor Optimization Digital Annealer in Industry 4.0

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On certain problems quantum computers can be disruptively faster than classical computers. After decades of research, the first quantum computers are coming onto the market and Fujitsu Digital Annealer – inspired by quantum annealers – is today an ideal platform for the development, testing and first application of quantum optimization algorithms.

To bring this new technology into the field we at Fujitsu co-create new solutions together with our clients. Teaming up with our customer Volkswagen we present how the digital annealer helps to reduce sound emissions for improved driving comfort. Other applications at Volkswagen optimize battery research for electric driving and machine utilization in line production.

In the talk we also give an introduction to the principles and the possibilities of the technology.

Speaker:
Andre Radon
Fritz Schinkel

Published in: Technology
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From Acoustic Design to Shop Floor Optimization Digital Annealer in Industry 4.0

  1. 1. Human Centric Innovation Co-creation for Success © 2018 FUJITSU Fujitsu Forum 2018 #fujitsuforum
  2. 2. © 2018 FUJITSU From Acoustic Design to Shop Floor Optimization Digital Annealer in Industry 4.0 Andre Radon Director IT Solutions Innovation Volkswagen Dr. Fritz Schinkel Fujitsu Distinguished Engineer Fujitsu
  3. 3. 2 © 2018 FUJITSU Quantum Computing at Volkswagen
  4. 4. 3 © 2018 FUJITSU Volkswagen looks beyond classical computing Quantum-assisted machine learning Reinforcement and machine learning  Financial market prediction  Self-driving vehicle  IT threat detection by clustering Quantum Simulations Complex physical / chemical simulations  Battery research  Molecule similarity search Time-critical optimization Design and construction  Acoustic design  Fluid simulations  Crash simulation Post-quantum security Evaluation of impacts on IT security issues  Shor’s algorithm
  5. 5. 4 © 2018 FUJITSU Annealing – Thermal, Quantum and Digital
  6. 6. 5 © 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!
  7. 7. 6 © 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
  8. 8. 7 © 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?
  9. 9. 8 © 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
  10. 10. 9 © 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
  11. 11. 10 © 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
  12. 12. 11 © 2018 FUJITSU Quantum Computers Neural Computers New Compute Architectures A hardware that can rapidly solve combinatorial optimization problems Digital Annealer General-purpose Computers
  13. 13. 12 © 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
  14. 14. 13 © 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
  15. 15. 14 © 2018 FUJITSU Job Shop Scheduling
  16. 16. 15 © 2018 FUJITSU Many scenarios for similar model  Scenarios  Logistics: Who delivers in which order  Capacity Mgt.: Resources binding and timing  Production planning: Machine assignment and order  Production optimization: Realtime job redistribution (dynamic factory)  Different objective function  Common model solvable by annealing
  17. 17. 16 © 2018 FUJITSU Job Shop Scheduling – an NP-hard problem  Planning input:  M machines: executing one operation at a time  N Jobs: processing sequence of M operations  N x M operations: specifies machine, defines duration  Planning target:  Starting times for all operations  Comply with planning input conditions  Minimum total production time
  18. 18. 17 © 2018 FUJITSU Job Shop Scheduling solved by Annealing Oper. 0 Oper. 1 Oper. 2 Job 0 M:0, D:1 M:1, D:1 M:2, D:1 Job 1 M:1, D:1 M:2, D:2 M:0, D:1 Job 2 M:1, D:1 M:0, D:2 M:2, D:1 Job 3 M:2, D:1 M:0, D:1 M:1, D:2 machine 0 machine 1 machine 2 Gantt diagram for job 0 1 2 3 time Job table (Operation, machine and duration) 0 0 1 2 3 1 0 1 2 3 2 0 1 2 3 machines job start time Bit model: possible operation start times 4 3 2 1 0 0 0 1 2 3 1 0 1 2 3 2 0 1 2 3 Solution: operation start times translate translate annealing
  19. 19. 18 © 2018 FUJITSU Technology Evaluation with Volkswagen  Used published QUBO 1)  Generated JSP test cases and QUBOs  Biggest test setup (1024 bits of DA V1)  N=10 jobs  M=7 machines  Model size in operative scale  Annealer runtime far below 1 second  Valid optimizations found 1) Quantum Annealing Implementation of Job-Shop Scheduling, Davide Venturelli, Dominic J.J. Marchand, Galo Rojo, arXiv:1506.08479v1 [quant-ph] 29 Jun 2015
  20. 20. 19 © 2018 FUJITSU Acoustic Optimization by Annealing
  21. 21. 20 © 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, …)
  22. 22. 21 © 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
  23. 23. 22 © 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
  24. 24. 23 © 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)
  25. 25. 24 © 2018 FUJITSU Summary and Outlook  Conclusion  Acoustic optimization and job shop scheduling are suitable problems for annealing.  Current bit capacity of Fujitsu DAU allows relevant model sizes.  Performance and solution quality are convincing.  Support by competent Fujitsu experts enabled fast evaluations.  Results of digital and quantum annealer are comparable from a user’s perspective.  Future steps  Build enhanced and new QUBO models in co-creation with Fujitsu.  Make use of new digital annealer V2.0.  Start quantum enabled pilot in operational context of Volkswagen.
  26. 26. 25 © 2018 FUJITSU … and 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 (Fujitsu Forum 2018)
  27. 27. 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

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