Digital Annealer

Introduction

July5, 2019

FUJITSU Quantum-Inspired
Computing Digital Annealer

Fujitsu Limited
Patent pending including related technology

Copyright 2019 FUJITSU LIMITEDReaching the Limit of Moore’s Law
Computers must process increasingly massive and complex data at higher and higher
speeds in order to support digital transformation in society and business. Moore’s Law* isapproaching its limit, threatening the drastic compute performance required in the coming future.
Data Volume

Power efficiency performance of general CPU “Relative value”
Performance improvement has
slowed due to power constraints

2002

Power
Efficiency
Performance

GAP

104
103
102
101

180nm

1

350nm

1990

40nm

90nm

The limit ofMoore’s Law

Unstructured
Data

2025

Moore’s Law foretells of improved
CPU density and performance

20nm
10nm
Structured
Data

250nm
2000

Today 2020

2010

Year

Quantum Computing is one promising prospect as a next generation computer*Moore's Law: An empirical rule in the semiconductor industry stating that the number of transistors in a dense integrated circuit doubles every 18~24 months.
1

Copyright 2019 FUJITSU LIMITED

Digital Annealer
A new architecture that solves "combinatorial optimization problems" athigh speed with digital circuits inspired by quantum phenomena
Digital Annealer
Quantum Computers
Easy to apply toactual problems
Still in the research stage ...

 Stable operation with digital circuit,and easy miniaturization
 Difficult to maintain a quantum state Easy mapping of more complex problems
with a fully-connected architecture Limits in connection and expansion
2

Copyright 2019 FUJITSU LIMITED

Digital Annealer Positioning
 Digital Annealer makes use of the annealing method, specialized for combinatorial optimization.
 Unlike quantum computers, Digital Annealer does not require an extremely low temperatureenvironment. Digital Annealer operates stably at room temperature.
MethodQuantum Computing Technology
Ising
Machine System
量子イジングマシン方式
Map a problem to a mathematical model of interacting magnets, then search for solutions
Specialized for combinatorial optimization problems
Annealing

Laser
Network System

Quantum Annealing

Digital Circuit

Optical Parametric
Oscillation

Superconducting Circuit

Fujitsu
Digital Annealer
Stable operation atroom temperature
Available in traditionalrack / server room

Hitachi, Ltd.
CMOS Annealing
MachineNational Institute of Informatics,
NTT and others
Japan Cabinet Office ImPACT"Quantum Neural Network"

NEC

Non-Quantum Architecture

D-Wave systems
D-Wave 2000Q

Development
Organization

Analyzed by Fujitsu

Quantum Gate System
Computation using quantum gates andquantum bits that can be in a superpositionof 0 and 1 states

IBM
IBM Q

GoogleMicrosoftIntel

Quantum Architecture
Requires cryogenic environment, but still technically unstable
Large cooling device required
3

Copyright 2019 FUJITSU LIMITEDWhat is the Annealing Method?
An algorithm based on the “annealing” metal processing phenomenon
Annealing Phenomenon

Annealing Method

When brought up to high temperature then graduallycooled, the structure of metal becomes stable (lowenergy).
Metal State= Unstable

High Temperature

High energy = Unstable atoms

Find a way to quickly fit all the pieces by firstshaking the whole system, then gradually
reducing the shaking= Stable

Low Temperature
Low energy = Stable atoms

When exploring optimal solutions, first search
all solutions including those far from optimal,
and then gradually close in to an optimal
solution.

Round-Robin Method
Check all combinations bymoving up in order to go
back down if a combination
does not work
4

Copyright 2019 FUJITSU LIMITEDCombinatorial Optimization Problems
Seek combinations or sequences that satisfy given constraints, with the goal of finding
the best out of all available combinations“Which is the shortest route that visits each city exactly
once and returns to the origin city?”

Example:The Traveling Salesman Problem
Number of cities: N

Constraint

City
City

City

Optimal Solution

City
City

Choose the number of cities

City

City
City

City

Visit each city only once
Evaluate based on travelling distance

City

City

City

Shortest route

With 5 cities  120 possible routes. With 32 cities  2.63x1035 possible routes

The number of combinations increases exponentially!
5

Copyright 2019 FUJITSU LIMITEDSolving Combinatorial Optimization Problems – An Example (1)Find the shortest-distance route (shortest path) that visits every city
exactly once and then returns to the starting point

Traveling Salesman Problem

1. Define an Optimum Solution for the
problem to be solved

2. Define Variables and Constraints
In order to find the shortest route, combine
the Variables:

Optimum Solution:
Shortest total
distance routeOrder
to visit&

Citiesto visit

City
City

City

Variables are the elements of the problem that must be defined inorder to seek optimum solutions.

City

With the Traveling Salesman Problem, the shortest route (minimum
value) is defined as the optimum solution, but optimum solutions for
combinatorial optimization problems can also be defined as
maximum values depending on the type of problem.

Define Constraints for the problem:

NEXT

NEXT After defining an optimum solution,
variables & constraints, how is the problem formulatedfor Digital Annealer?

visit every city exactly once

Which pieces of information are
combined in order to lead to the shortest route?
6

Copyright 2019 FUJITSU LIMITEDSolving Combinatorial Optimization Problems – An Example (2)Find the shortest-distance route (shortest path) that visits every city
exactly once and then returns to the starting point

Traveling Salesman Problem

3. Create a formula that can be solved with Digital Annealer

Create an Ising model for formularization
Ising Model: Used to express the interactions of variables with “spins” of either 0 or 1.
Cities to visit (i.j)
Variable
City A City B City C City D City E
City A City B City C City D City E
VisitOrder(t) 0
0
1
2
3
4
1
2
3
4
0

x

00

x

1

x

10

2

x

3
4

01

x

02

x

03

x

x

11

x

12

x

13

20

x

21

x

22

x

x

30

x

31

x

32

x

40

x

41

x

42

Constraints •Visit only one city at a time

•Visit every city exactly onceHere
0 1 means:
0visit City B first,
then visit City A,
then
0 D, C and0
finally E.

04

0

0

1

0

x

14

1

1

0

0

23

x

24

2

0

0

0

1

0

x

33

x

34

3

0

0

1

0

0

x

43

x

44

4

0

0

0

0

1

Only one selection
per column/row

・Eachrepresents a single bit
・2nd generation Digital Annealer supports up to 8,192 bits

4. Use Digital Annealer to find solutions

Send formula to Digital Annealer toobtain optimum solution (values)
Objective Function
E = Function to minimize/maximize energy

Variablest: Visit orderi j: City to visit

Constraint Terms
Constraint

Visit only one city at a time:

Constraint

Visit every city exactly once:

・A bit can be 0 (in the city) or 1 (not in the city)
・ 1 represents the city to visit on the nth leg of the route(i.e. the optimum solution)
 Digital Annealer seeks solutions to this type of problem
7

Copyright 2019 FUJITSU LIMITEDCombinatorial Optimization Problems Across All Industries & Business
Advanced
Healthcare

Autonomous
VehiclesNew Material
Development

Utilities
Management

DemandForecasting

Materials &Compounds

Network Config
Management

Hospitality
Management

Drug
Discovery

Portfolio
Management

DigitalMarketing

Applicable to New Areas

Distribution RoutePlanning

Improve Precision and Reduce Timeto solve existing combinatorial
optimization problems
Logistics

Applicable Area Examples
8

Copyright 2019 FUJITSU LIMITED

Digital Annealer Application Cases
Search for molecular similarity
Chemical /Pharmaceutical

Big Data Visualization Toolkit

Seek overall similarity of compounds

Data Analysis

Investment Portfolio (QHRP)

Traffic Optimization

Logistics

Select non-overlapping distribution routes
for vehicles driving to each destinationfrom multiple departure locations

Financial

Optimizing HR Planning
Manufacturing

Clustering of big data for visualization

Select investment portfolio assets
which are not affected by correlations

Production Planning Optimization

Optimize overall operation scheduling of multiple
machines performing tasks with interrelatedsequences

Develop HR planning according to staff
requests, capabilities, desired schedule,
attendance conditions, etc

Optimizing Inventory Allocation Optimizing Flow Line in Factory
Optimize combinations parts and products
to meet destination requirements
9

Select optimal shelf arrangement and parts pickup routes within factories
Copyright 2019 FUJITSU LIMITEDChemical &Pharmaceutical

1. High Precision Molecular Similarity Search for Drug Discovery
Contributing to the development of highly effective medicines
IssuesUse Case

The conventional Finger Print method determines the presence or absenceof an atomic group, but does not consider the molecular shape. Thus, aprecise search cannot be performed.

Conventional
MethodFinger Print: A method of representing the presence or absence of an atomic group as 0 or 1
and expressing the molecule as a Boolean vector

Finger Print
Matching without
consideration ofmolecular shapeTechnique
By converting the molecular structure to a graph and handling atomic
groups as nodes and bonds as edges:• Precision is improving by considering molecular shape• Calculations are performed at high speed by Digital Annealer

Digital Annealer Benefit
Graph Theory

Results

Highly accurate bymatching molecularshapes• Highly precise molecular similarity search becomes possible• Expected to improve the efficiency of drug development leading to newhighly effective medicines

Combinations of atomic groups results
in a huge volume of calculations
10

Improve efficiency
with Digital Annealer
Copyright 2019 FUJITSU LIMITED2. High-Speed Clustering for Big Data Utilization
MarketingVisualizing large-scale datasets for more accurate analysis
IssuesUse Case

As the importance and prevalence of big data increases, highspeed data processing is necessary to effectively derivebusiness insights

Conventional

Digital Annealer

Customer data for
40,000 people

Grouped by similar data

TechniqueHigh precision clustering with hierarchical structures is
implemented by compressing high dimensional data andsegmenting it into portions that can be clustered

Highly accurate clustering with Digital Annealer
Fine grained

Results

Changeable according to
the level of clustering• Clustering is accelerated from several hours with conventional methods tojust a few minutes with Digital Annealer• Large scale data sets can be visualized and analyzed• The level of clustering can be changed to enhance analysis

Coarse grained

11

Copyright 2019 FUJITSU LIMITED

Logistics

3. Route Optimization to Reduce Traffic Congestion
Reduce overall travel time by distributing routes throughout a cityor factory to avoid congestion
IssuesUse Case
Conventional

With conventional routing systems, there is atendency to assign the shortest distance route,leading to traffic congestion in the city center

TechniqueDegree ofCongestion• Optimize route selection to avoid overlap• Prioritize route options by adding conditions, such as: speed
limits, number of lanes, etc.

Select Shortest
Path

High

Digital Annealer

ExampleMax
Speed(km/h)
1020
40

Degree ofCongestion

Optimized
Route Selection

High

20
40
50

50
Low

ExampleMax
Speed(km/h)
10

Low

60

60

150

Total Travel Time

Results

100• Reduce traffic congestion by up to 40% by dispersing traffic• Apply to cases of iterative simulation used for road development planning• Applicable to other routing problems, such as warehouse collection anddistribution, AGV (Automated Guided Vehicles), and network traffic

50
0

12

Shortest
Route
Method101hrs

40% Decrease
DigitalAnnealer
58hrs

Immediately recalculate routes
according to traffic condition changes

Total Travel time
Shortest RouteWith Digital Annealer

101hours
58hours

Copyright 2019 FUJITSU LIMITED4. Investment Portfolio Optimization Through Risk Diversification
Finance

Instant clustering for the correlation of 500 stocks to compose arisk-resistant portfolio
IssuesUse Case

The commonly used Minimum Variance (MV) method for
portfolio optimization is susceptible to the influence of market
fluctuation

S&P500 Investment Results (2005 – 2011)

Results (Sharpe ratio comparison)

QHRP

Risk/Return Ratio

MV

Technique

Lehman ShockQuantum-inspired Hierarchical Risk Parity (QHRP) portfolio
optimization method provides for:
• The clustering of assets into a tree diagram based on riskcorrelations• Composition of risk-diversified portfolios with low correlativity

Digital Annealer Technique
Asset Price Change Variance Matrix
Company Company Company

Results

B

C

Company A

1

0.23

0.85

Company B

0.23

1

0.64

Company C

0.85

0.64

1

…

• Create portfolios with resistance to market fluctuations that
continue to provide stable returns• 60% higher Sharpe Ratio compared to MV method

A

…

…

Express all correlations among assets(price changes between two assets) using
variance-covariance matrices
13

Clustering Assets from Correlations

Companies: A

C

D

B

Copyright 2019 FUJITSU LIMITED5. Manpower Management
Manufacturing

Optimize manpower management and eliminate dependencies on individual skills
Use Case

Issues• Securing correct manpower difficult due to increases in
conditions to be considered• Difficult to quickly respond to sudden changes in schedulingconditions• Manpower management-related shift planning dependent on
specialist skills

Shift Planning Example
Monday220

Planned Workload

T1

T2

T3

T4

T1

T2

T3

T4

T1

T2

T3

T4

T1

T2

T3

T4

Work days
per staff(008):

×

×

×

×

×

×

◎ ○

×

×

×

×

×

×

×

◎

4

8

(012):

×

×

×

○

×

×

○

○

○

○

○

×

×

○

×

○

0

24
25

(005):(014):

×
○

×
○

×
○

○
×

○
○

○
×

×
×

×
×

×
×

×
○

○
○

○
○

×
○

○
○

○
×

○
○

0
0

32

(010):

◎

×

×

×

×

○

×

◎

×

◎

×

×

5

(012):

-

◎

×

◎ ○
○ ○ ◎

×

33

×

×

◎

×

○

×

×

○

×

◎

Worker Capacity

Technique• Digital Annealer’s 8192-bit scale & 64-bit gradations handle
detailed requirements simultaneously: e.g. preferred dates,
consecutive day work restrictions, and 5-day work week
• High-speed processing allows for prompt recalculation when
staffing conditions changeID

per day0Workload (gapfrom plan)

Results

Sunday180

Saturday200

Tuesday220

221 (1)×

222 (2)～199 (-1)

180 (0)5# of workers:
29 (-5)◎: Determined Shift, ○: Preferred, ×: Not Preferred• Shift planning requirement reduced from 34 staff to 29 after
optimization• Work dependent on individual skills reduced, and shiftplanning completed quickly• Applicable to work style reform in various industries.

Before:34 Workers Required
14

With Digital Annealer:
29 Workers Required
Copyright 2019 FUJITSU LIMITED6. Production Control Scheduling
Manufacturing

Optimize equipment allocation to reduce production process time
Use Case

Issues
Job Process Table• Varied product types leads to different process times, requiring
efficient utilization of equipment to reduce production times• Allocation is time consuming as jobs are allocated manually

Process0

Process5

Process6

Time

MachineTime

MachineTime

Machine

Time

4
1
3

2
3
2

6
4
5

2
3
2

1
2
1

3
1
2

2
6
2

3
3
3

job0
job1
job2

Technique

Process ~Process1

Machine

~

~

~

~

job7
6
3
2
3
4
3
0
1
job8
1
2
5
2
6
2
3
3
job9
3
3
0
2
4
3
2
1
• Each machine can process only 1 job at a time
• Each job proceeds in order of process number• Job processing time varies depending on machine/process• High-speed processing enables instant optimization, even whensudden machine failures occur or jobs requests change• Digital Annealer’s 8192-bit scale & 64-bit gradations handle
multiple jobs, varying process times, and process-combination
conditions

Job Allocation - Before
1Machine0
Machine1 job1
Machine2
Machine3 job2
Machine4 job0
Machine5
Machine6

Results• Efficient equipment allocation leads to a 30% reduction inprocessing time
• Allocation plans created quickly and without the need forspecialist skills• Applicable to equipment verification and the purchasing of new
equipment• Also applicable to various industries with scenarios consisting of
the combination of multiple processes

2

3

4

5
job0

6

7
job1

8

9
10 11
job5
job2

job8
job3
job3
job5
job6
job5 job6
job0
job4
job3 job1
job2
job2
job4
job0
job0
job7
job2

27

~

28

29

30 31
job8

job5

2

Machine0
Machine1 job8
Machine2
job5
Machine3 job5 job9
Machine4
Machine5 job4
Machine6 job7

15

3

4
job1

5
job9

6

7
job5

34

35

36

41

job7

job9

job9

27

job9

30% Reduction
28 29
job7

job5
job2
job3
job6

job2
job6

40

job9

job8

~

38 39
job7

job8

job8
job7

9
10 11
job0
job1
job9
job3
job3
job7
job4
job0

37

job9

job5

8

job6
job6 job2
job0
job3 job1
job9 job8
job0

33

job7
job7

Job Allocation – with Digital Annealer
1

32

30

31

32

33

34

35

36

37

38

39

40

From 41 hours down
to 29 hours

job4

Copyright 2019 FUJITSU LIMITED41

7. Inventory Allocation Optimization
Manufacturing

Optimize parts allocation and inventory for the assembly of multiple product models
Use Case

Issues• Prioritize allocation to products with high unit price and profit• Prioritize allocation to high-demand products• Due to increases in product type variation, complexity growing
for parts selection from inventory. Enormous time required tocreate production parts combinations.
• Manual optimization difficult due to complexity

Priority Product(High)A

Technique

3 parts 2 parts• Digital Annealer’s 8192-bit scale & 64-bit gradations handle
large variety of products, parts and production conditions• High-speed calculation enables real-time parts allocation

Use newestparts

Results

Product
B. . . .

ProductN

1 part2 parts
2 parts

Use newest
parts possible

3 parts 1 partUse older parts
from inventory

Warehousedin September• Real-time recognition of parts that meet conditions, improving
efficiency of inventory and quality control• Eliminate work dependent on specialist skills• Also applicable to retail and distribution warehouses
16

Warehousedin AugustWarehousedin July

Warehousedin MayWarehousedin JuneWarehousedin March
Warehousedin January

Warehousedin AprilWarehousedin MayPart APart B

Warehousedin March. . . .

Warehousedin March

Part N8.Factory Parts Pick Up Optimization
Manufacturing

Reduce travel distance for warehouse parts pick up by up to 45%

Now in use at Fujitsu IT Products

IssuesUse Case• High-mix, low-volume factory production requires a large varietyof parts for each product. Time and labor required dependent on
the experience level of each worker• Inconsistent and inefficient parts pick up process

Very complicated parts picking routes
required experienced workers
Warehouse area: 1000m²
Number of parts: 3000

Technique• Routes and shelf population are minimized as combinatorial
optimization problems• Correlation of frequently used shelves identified

Digital Annealer provides optimum
picking routes displayed on a tablet

Results• Even inexperienced workers can realize efficient parts picking• Travel distances reduced by up to 45% per month through route andshelf location optimization• Optimization methods to be deployed to other factories, as well asother processes such as warehouse management
17

Copyright 2019 FUJITSU LIMITED8. Case Study:
Manufacturing

Factory Parts Pick Up Optimization - At Fujitsu IT ProductsCompany Profile
Company Name

Fujitsu IT Products Limited

Location

1-1, Kasajima-to, Kahoku-shi, Ishikawa, 929-1196, Japan

Capital

100 million yen (wholly owned subsidiary of Fujitsu Limited)

Establishment
Industry

April 1, 2002

Employees

455 people

Manufacture of servers, supercomputers, storage systems , software, etc.
Source: http://www.fujitsu.com/jp/group/fjit/ (Japanese)

Digital Annealer Project Schedule
Discussion from
September 2017

PoC from
October 2017

Servicein February 2018

Case Study: http://www.fujitsu.com/global/digitalannealer/case-studies/201804-fjit/
18

Copyright 2019 FUJITSU LIMITED8. Case Study:
Manufacturing

Factory Parts Pick Up Optimization - Configuration
Goal: Leverage existing operation, and create a new shelf position database
to use Digital Annealer to calculate optimal pick up routes

BeforeSales

Component Parts DB
Product A-Part 1
-Part 3
-Part 6

Order Input

Product A

Factory
TabletShelf a → c → f
Part 1 → 3 → 6

Shelf Position DB
Part1: Shelf a
Part3: Shelf cPart5: Shelf eWith Digital
Annealer

Part1: Shelf a
Part3: Shelf cPart6: Shelf f

Part2: Shelf bPart4: Shelf dPart6: Shelf f

Based on information displayedon the tablet, parts picked up

4 Output Optimal route
Pick up done basedon the best route5

3 Optimum route provided

Management
Server1

Shelf positions and distances
between shelves are put in
the Shelf Position DB

Newly Implemented

Shelf ac: 2
Shelf cf: 5
Shelf af: 7

Part1: Shelf a
Part3: Shelf cPart6: Shelf f

Shelf position
①
②
③

④
⑤

TabletShelf c→f→aPart 3→6→1⑥

Shelf ac: 2
Shelf cf: 5 ＋ Mathematical
Shelf af: 7
Expression

Distance
between shelves
a
b
c
d
e
f

19

b
1
0
1
4
5
6

c
2
1
0
5
4
5

d
3
4
5
0
4
4

e
6
5
4
4
0
1

f
7
6
5
4
1
0

Digital Annealer
Cloud Service
1

Shelf Position
DB

a
0
1
2
3
6
7

Digital Annealer

2

Request a formula anddistance between shelvesfrom Digital Annealer via
REST-API

1

1

0

0
1

1
0

Copyright 2019 FUJITSU LIMITED

Digital Annealer Advantages
Applicable to real world problems with the stability andbalance of scale, connectivity, and precision
Scale

Connectivity

Ready to scale up for 8,192bit problems

Easy to use with total bit coupling

Precision

High precision with 64bit graduations

Stability

Stable operation at room
temperature with digital circuits
20

Copyright 2019 FUJITSU LIMITEDUnique Technology Improves Optimum Solution Accuracy
Digital Annealer increases the probabilityof finding optimum solution

Rapidly find optimum solution with
Digital Annealer parallel evaluation

Evaluation

Optimum Solution

1

Offset2
Value

Escape!

Trial 1

Trial 2

Bit 1
Inversion

Bit 2
Inversion

Determine
Evaluation
Value

Determine
Evaluation
Value

Trial N・・・

Bit N
Inversion

Local Solution

Optimum Solution
Search SpaceState

1 Detect localized solution reached

Determine
Evaluation
Value

Evaluating the bit inversions for each trial inparallel, leads rapidly to the optimum solution

2 Apply offset value

Encourage escape from local solutions, leading to optimum solution
21

Copyright 2019 FUJITSU LIMITEDSpecification Comparison
Applicable to real world problems with the stability and balance ofscale, connectivity, and precision

Implementation
Technology

Number of Bits

Digital Annealer(2nd generation)

Company A

Company B

Digital Circuit

Superconductive Circuit

Non-Linear Optical(Using existing technology)(Cryogenic cooling required)(1km ring device)

8192

2048

2048

Amount of Coupling

Total Coupling

Evaluation
Accuracy

64bit Gradations

6 - Partly Coupled(64bit total coupling
equivalent)

Total Coupling

32 Gradations

3 Gradations

22

Copyright 2019 FUJITSU LIMITED

Digital Annealer
Services Overview

Copyright 2019 FUJITSU LIMITED

Digital Annealer Service Overview (Japan Market)
 Cloud Service & Technical Service launched in May 2018
 On-Premises Service launched in February 2019
Deliver high speed processing for combinatorial
optimization problems

FUJITSU Quantum-Inspired
Computing Digital Annealer

FUJITSU Quantum-Inspired
Computing Digital Annealer

Cloud Service

On-Premises Service

Support for Digital
Annealer utilization

Digital Annealer
Technical Service

Combinatorial Optimization Problems Faced in Actual Business24

Copyright 2019 FUJITSU LIMITEDFUJITSU Quantum-Inspired Computing Digital Annealer

Digital Annealer Cloud Service
General Formula Type

Solution-Specific Type(QUBO API)

(Optimization Solutions API)

Submit Ising model (QUBO) formulas throughWeb API

Submit solution-specific data and receive
optimal solution

Customer Site
RequestCustomer
Definition &Formulation
・Ising Modeling・Variables
・Constraints

Web API(General Formula)

Fujitsu Datacenter

Customer Site

DigitalAnnealer
Infrastructure

Customer

Fujitsu Datacenter
Request

Solution-Specific
Data

Find Optimal
Solutions

Optimum
Solution

Web API(Solution-Specific)

DigitalAnnealer
Infrastructure

Find Optimal
Solutions

Optimum
Solution25

Copyright 2019 FUJITSU LIMITEDFUJITSU Quantum-Inspired Computing Digital Annealer

Optimization Solution API
Warehouse Pickup Optimization API
A Web API service that finds the shortest distance route for picking up
specified products from multiple locations within a warehouse• A list of products to pickup is input and the optimal pickup order is output• Supports up to 32 locations with the same priority• Warehouse map files (coordinate data) are pre-registered in the cloudCustomer Site

Fujitsu Datacenter
RequestCustomer
D

C

B

E

F

A

1. Rack A
2. Rack B
3. Rack C

Warehouse Management
System・Map
・Product List

Web API(Solution-Specific)

DigitalAnnealer
Infrastructure

Find Optimal
Solutions

Optimum Solution…
26

Copyright 2019 FUJITSU LIMITEDFUJITSU Quantum-Inspired Computing Digital Annealer

Cloud Service Menu (Japan Domestic Market Only)
Service Type

Fee

Remarks

Premium

Basic fee (fixed monthly)

Usage time is not limitedStandard

Basic fee (fixed monthly)＋
Metered rate (by usage)

Processing time is meteredGeneral Formula Type（QUBO API）

Solution-Specific Type

（Optimization Solutions API）

Warehouse Pickup
Optimization API(calculated in seconds; data transfer
time is not included)

Individual quotation

27

Copyright 2019 FUJITSU LIMITEDFUJITSU Quantum-Inspired Computing Digital Annealer

Cloud Service Web APIs

Synchronous Web API (basic) and Asynchronous Web API (optional) offerings:
Synchronous Web API
Customer

1

0

0

1

Response(with Acceptance ID)

Request for result
corresponding to ID

OptimalSolution0

Digital Annealer
Cloud Service

Request…

 Ising Model Mathematical
Expressions
 Variables
 Constraint
Conditions

1

1

1

Customer

Network

OptimalSolution

Network

RequestDigital Annealer
Cloud Service…

 Ising Model Mathematical
Expressions
 Variables
 Constraint
Conditions

Asynchronous Web API

1

1

1

0

0
1

1
0

The API requests calculation results synchronously.
The API returns when the calculation process has completed.

A calculation request is issued and the API returns.
A separate request is issued to obtain the calculation result.

The problem is processed in real timeMultiple requests can be made for large problems

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Copyright 2019 FUJITSU LIMITEDFUJITSU Quantum-Inspired Computing Digital Annealer

Help Desk Service

Service Details for Digital Annealer Cloud Service Subscribers:
Service Hours

Weekdays 9:00-17:00 (JST) *Closed on Japanese holidays

Inquiry Contact Method

Email

Acceptable Questions• Questions about specifications, settings and usage of the Digital Annealer Cloud Service• Questions, investigation, workarounds when the Digital Annealer Cloud Service does not function correctly

Trouble Notification
DetailNotification from the time the issue
occurs, functions impacted, etc.

Method Portal/Email

Language

Japanese

Maintenance Notification

Service Detail / Update Guide

Detail Notification of Digital Annealer Cloud Service
planned or emergency maintenancedate/time, functions impacted, etc.

DetailMethod Portal/Email

Method Portal/EmailNotification of new functionality andimprovements made to the Digital
Annealer Cloud Service

Important Notes
Inquiries received outside service hours will be processed after 9:00am on the following business day

The following inquiries are excluded from the Help Desk Service:
・Processing speed tuning (performance evaluation resulting from customer’s APP design, implementation and operation, etc.)
・Consulting (advice on creation, design, implementation and operation of mathematical models)・Disclosure of information related to our cloud service environment and logs
・Calculation result accuracy

Service Fee

Calculated as 5% of the Web-API usage service fee
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Copyright 2019 FUJITSU LIMITEDFUJITSU Quantum-Inspired Computing Digital Annealer

Digital Annealer On-Premises Service Offering
 Digital Annealer Server installed at the Customer site for a monthly subscription
 Supporting 8,192-bit full connectivity and flexible partitioning for parallel operation andscaling to match problem size and precision requirements.

Customer

Definition & Variable
Formulation
 Formula• Ising Modeling• Variables• Constraints

RequestDigital Annealer
ServerWeb API

Optimum Solution

Find Optimal
Solutions

Digital Annealer
ServerCustomer
Server Room/Datacenter
Contact sales for availability of the on-premises model in your region

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Copyright 2019 FUJITSU LIMITED

Digital Annealer Technical Service

Digital Annealer Technical Service - Outline
Technical experts with advanced mathematics knowledge and data analysis capabilities support
you in resolving your optimization problems

Digital Annealer Technical Service
Implementation Phase
Assessment
Convert customer issues
into formula

Training
TuningSupport

Deployment Support

Operation Phase
Implementation
Application
implementation

Apply formulation tocustomer issues

Operation
Maintenance,
SupportTraining for Engineers
Tuning Support Service

Complete support from introduction to operation, leveraging Digital Annealer for customer business
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Copyright 2019 FUJITSU LIMITED

Digital Annealer Technical Service

Digital Annealer Technical Service Details
Formulation Verification

Development Support

Implementation

Verify the customer’s problem can beconverted to a combinatorial
optimization problem, and generate amathematical model to solve with
Digital Annealer.

PoC planning for customer Digital
Annealer use. Evaluate mathematicalmodel implementation and results.
Support requirements definition forcustomer Digital Annealer deployment.

Develop and deploy application for
use with Digital Annealer: Deploy on
customer systems, establish
connection to Digital Annealer,
process input / output data, andperform post processing of output.

Operation

Training

Tuning Support

Answer questions and solve problems
for the deployed Digital Annealer
application.

F2F instructor-led Digital Annealer
training:• Basic: Digital Annealer features andmechanisms• Hands-on: Solve optimization
problems using Digital Annealer
Cloud Service
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Problem solving support including
parameter tuning for Digital Annealer
Cloud Service customers.

Copyright 2019 FUJITSU LIMITED

Digital Annealer Technical Service

Digital Annealer Project Example
Typical Project Duration: 4-6 months
2~3 months
Pre-Phase• Procedure
Agreement• Conclude NDA(As needed)

Available
Digital Annealer
Services

2~3 months

Phase 1

Phase 2Phase 3Phase 4

Technical Check

Select businesstarget for PoC

Model Construction

PoC• Pre-meeting(2-3 times)• TechnicalIntroduction• Technical
Verification• Select target
business• Agreement on
Expectations• PoC Plan
Agreement• Formulation• Ising Model
Construction

Next
Step

• PoC on Digital
Annealer-Confirm Functions-Performance, UX, etc• PoC Result Summary

Cloud Service: Maximum 6 months trial (Fee-based)

Premium/Standard
Cloud Service (Fee-based)

Technical Service (Fee-based)
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Copyright 2019 FUJITSU LIMITED

Digital Annealer
Mitsubishi UFJ Trust
Case Studies
Investment Technology

Fixstars Corporation

Institute Co., Ltd.
Fujifilm Corporation

Touhoku University

Technical verification of
portfolio optimization in asset
management

Technical verification of
production lines for high-mix
low-volume models infactories

A new service in thequantum computing area

Participation in
the MITOU Programof METI / IPA

RecruitCommunications
Co., Ltd.

Solving combinatorial optimization
problems to control AutomatedGuided Vehicles (AGVs)

New marketing technology
research and development for
real-world business problems

Using Digital Annealer to foster
talent and start-up companies
in technical fields
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Copyright 2019 FUJITSU LIMITED

Digital Annealer
Roadmap

Copyright 2019 FUJITSU LIMITED

Digital Annealer Roadmap
2018

2019
Technical Service

1st Gen

Cloud

May

1024 bit
Precision: 16 bit
Scale:

65536 Gradations

2nd GenCloud Dec

On-Premises Service 4Q

Next Generation

8192 bit
Max Precision: 64 bit
Max Scale:

1.845x1019 Gradations

DigitalAnnealing Unit

Large-Scale Parallel
Processing

Expand applications from technical verification to real-world business value
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Copyright 2019 FUJITSU LIMITEDApplication Areas Surpassing the Competition - 2nd Generation
Precision (bit)

2nd Generation

64

Priority for precisionand priority for size
can be switched

bit

16
bit

1st Generation

Priority for Size

Company A
Company B
Company C

8192
bit

1024
bit
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Size (bit)Copyright 2019 FUJITSU LIMITEDApplication Areas Surpassing the Competition – Next Generation
Develop further large-scale application technology

Accuracy
Precision

64
64
bit

2019

Supports up to 1,000,000 bitswith software partition technology& multichip support

2nd
2nd Generation
generation

bit

16
16
bit
bit

1st1generation
st Generation

1,000,000 bit

Priority
for Size
Priority
for size

8192
8192
bitbit

1024
1024
bit
bit

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Size
Copyright 2019 FUJITSU LIMITEDTackling Extremely Large Scale Problems
Fujitsu is developing a problem-splitting method to solve extremely large-scale problems
This method extracts a portion of a problem according to problem characteristics; processing each portion on Digital Annealer, then assembling theoutput from multiple calculations to derive the total optimal solution using search flow.

 This method allows a single 2nd generation (8192 bit) Digital Annealer to solve 100,000 bit scale problems
 Further software and hardware enhancements will expand the scale to 1 million bits (planned for CY2019)

Large scale problems split into manageable portions by software
Depending on the characteristicsof the problem, multiple splitting
methods available

DAU

Find the optimal solution
using a solution search flow

DAU

DAU

DAU

Hardware technology links multiple Digital Annealer Units (DAUs) to execute large scale parallel processing
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Copyright 2019 FUJITSU LIMITED

Digital Annealer Global Rollout Global service now available On-Premises Service launched in 2019 (Global launch planned)
EMEIA

Americas• Cloud Service• Cloud Service• Technical Service

Japan

Launched in FY 2019*• Cloud Service

APAC /
Oceania• Technical Service
Now Available• Technical Service• On-Premises Service
Now Available• Cloud Service• Technical Service
Launched in FY 2019**Please contact us regarding theservice start date in each country.
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Copyright 2019 FUJITSU LIMITED

Digital Annealer
Partnerships

Copyright 2019 FUJITSU LIMITEDExpanding Digital Annealer Applications Through Partnerships

Collaborative researchin cutting-edge areas

1QBit middleware implemented
on Digital Annealer

Confronting new issues
in society
Promotion of combinatorial
optimization as a way tosolve societal issues

Expansion of
application areas

Digital Annealer

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Copyright 2019 FUJITSU LIMITEDPartnership with 1QBit
 The world’s #1 vendor of software for quantum computers
 Conducting joint business around the world
 Digital Annealer incorporated in 1QBit Cloud Service in FY2019

Digital Annealer
Middleware

HardwareMathematical formulas
and algorithms forcomputation

The processing power
to solve problems
with high speed andhigh precision

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Copyright 2019 FUJITSU LIMITEDPartnership with University of Toronto World-class research university in the fields of AI and quantum computing
 New joint research center established with the University of Toronto in November, 2017:

Fujitsu Co-Creation Research Laboratory at the University of Toronto Joint research in smart transportation, networks, finance, and healthcare fields

Smart Traffic

Network

Finance

Medical44

Copyright 2019 FUJITSU LIMITEDJoint Research with the University of Toronto- Cancer Radiation Therapy
 Huge computational load required for simulation before a treatment plan can be made.
 Current technology needs multiple hours to a few days to calculate the combinatorial optimum.
Digital Annealer takes only a few minutes

Huge number of irradiation patterns (numberof combinations) with variations such as range,
direction, and intensity of irradiation

Intensity Modulated Radiation Therapy (IMRT)
Vital Organs

Even when the beams are from only one
direction, the number of
combinations would be:

Cancer10150

In case of irradiation against a 1cm2 tumor with a precision of 1mm2 and
32 intensity levels from one direction
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Copyright 2019 FUJITSU LIMITEDFUJITSU Digital Annealer

The world’ s first
Quantum-Inspired technology
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Copyright 2019 FUJITSU LIMITED