From Web Services to Quantum Computing: The Power of Building on the Cloud

Slide 1

Slide 1 text

Slide 2

Slide 2 text

Slide 3

Slide 3 text

Slide 4

Slide 4 text

Slide 5

Slide 5 text

Slide 6

Slide 6 text

Slide 7

Slide 7 text

Slide 8

Slide 8 text

Slide 9

Slide 9 text

Slide 10

Slide 10 text

Slide 11

Slide 11 text

Slide 12

Slide 12 text

Slide 13

Slide 13 text

© 2020, Amazon Web Services, Inc. or its Affiliates. 3 it’s better than 2, elasticity is better than any number Amazon EC2 Auto Scaling Amazon RDS Database Application Load Balancer Customers Amazon Aurora RDS for MySQL RDS for PostgreSQL RDS for MariaDB RDS for Oracle RDS for SQL Server

Slide 14

Slide 14 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Scaling the database is more difficult Amazon EC2 Auto Scaling Amazon Aurora Database Application Load Balancer Customers Amazon Aurora MySQL or PostgreSQL compatible

Slide 15

Slide 15 text

Slide 16

Slide 16 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Database freedom Amazon Aurora MySQL or PostgreSQL compatible Amazon DynamoDB Amazon DocumentDB with MongoDB compatibility Amazon Neptune (Graph) Amazon QLDB (Ledger) Amazon Keyspaces (for Apache Cassandra)

Slide 17

Slide 17 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Using containers Amazon Aurora MySQL or PostgreSQL compatible Amazon DynamoDB Amazon DocumentDB with MongoDB compatibility Amazon Neptune (Graph) Amazon QLDB (Ledger) Amazon Keyspaces (for Apache Cassandra) Amazon Elastic Container Service Amazon Elastic Kubernetes Service Container Container Container Container Container

Slide 18

Slide 18 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Tasks, Pods, and Services Amazon Aurora MySQL or PostgreSQL compatible Amazon DynamoDB Amazon DocumentDB with MongoDB compatibility Amazon Neptune (Graph) Amazon QLDB (Ledger) Amazon Keyspaces (for Apache Cassandra) Amazon ECS Amazon EKS Task Pod Service

Slide 19

Slide 19 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Managing containers, not clusters Amazon Aurora MySQL or PostgreSQL compatible Amazon DynamoDB Amazon DocumentDB with MongoDB compatibility Amazon Neptune (Graph) Amazon QLDB (Ledger) Amazon Keyspaces (for Apache Cassandra) Amazon ECS Amazon EKS Task Pod Service AWS Fargate

Slide 20

Slide 20 text

Slide 21

Slide 21 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Using functions for web applications AWS Lambda Function Function Function Amazon API Gateway Application Load Balancer AWS SDK Lambda Invoke API sync/async

Slide 22

Slide 22 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Adding storage… AWS Lambda Function Function Function Amazon API Gateway Application Load Balancer AWS SDK Lambda Invoke API sync/async Amazon S3 Bucket HTML, CSS, JS Images

Slide 23

Slide 23 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Adding storage and authentication AWS Lambda Function Function Function Amazon API Gateway Application Load Balancer AWS SDK Lambda Invoke API sync/async Amazon S3 Bucket Amazon Cognito User Auth HTML, CSS, JS Images

Slide 24

Slide 24 text

Slide 25

Slide 25 text

Slide 26

Slide 26 text

Slide 27

Slide 27 text

Slide 28

Slide 28 text

Slide 29

Slide 29 text

Slide 30

Slide 30 text

Slide 31

Slide 31 text

Slide 32

Slide 32 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Availability and scalability – Fully managed services Region Availability Zone Availability Zone Availability Zone API Gateway Lambda Function DynamoDB Table S3 Bucket

Slide 33

Slide 33 text

© 2020, Amazon Web Services, Inc. or its Affiliates. AWS operational responsibility models On-Premises Cloud Less More Compute Virtual Machine EC2 Elastic Beanstalk AWS Lambda Fargate Databases MySQL MySQL on EC2 RDS MySQL RDS Aurora Aurora Serverless DynamoDB Storage Storage S3 Messaging ESBs Amazon MQ Kinesis SQS / SNS Analytics Hadoop Hadoop on EC2 EMR Elasticsearch Service Athena

Slide 34

Slide 34 text

Slide 35

Slide 35 text

Slide 36

Slide 36 text

Slide 37

Slide 37 text

Slide 38

Slide 38 text

Slide 39

Slide 39 text

Slide 40

Slide 40 text

Slide 41

Slide 41 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Preparing the data to be analyzed Raw Data AWS Glue Glue Crawler Glue Data Catalog Glue Job Prepared Data Amazon Athena Amazon Redshift Load / Query Query Extract, Transform, and Load (ETL) Analytics Applications

Slide 42

Slide 42 text

Slide 43

Slide 43 text

Slide 44

Slide 44 text

Slide 45

Slide 45 text

Slide 46

Slide 46 text

Slide 47

Slide 47 text

Slide 48

Slide 48 text

Slide 49

Slide 49 text

Slide 50

Slide 50 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Supervised / Unsupervised Learning Data Notebook Model Train Amazon SageMaker Ground Truth Labeled Data Amazon SageMaker Predictions Amazon SageMaker Autopilot Amazon SageMaker Experiments Amazon SageMaker Debugger Amazon SageMaker Model Monitor Amazon SageMaker Studio

Slide 51

Slide 51 text

Slide 52

Slide 52 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Adding human feedback to the loop Data Notebook Model Train Amazon SageMaker Ground Truth Labeled Data Amazon SageMaker Predictions Amazon SageMaker Autopilot Amazon SageMaker Experiments Amazon SageMaker Debugger Amazon SageMaker Model Monitor Amazon SageMaker Neo Amazon Augmented AI Human Feedback Amazon SageMaker Studio

Slide 53

Slide 53 text

Slide 54

Slide 54 text

Slide 55

Slide 55 text

Slide 56

Slide 56 text

Slide 57

Slide 57 text

© 2020, Amazon Web Services, Inc. or its Affiliates. “Qubits” can still have 2 different states, but they can be in superposition Qubit #1 Qubit #2 Value Qubit #3 Qubit #4 It’s | ⟩ 0 50% of the time, | ⟩ 1 the other 50% It’s | ⟩ 0 90% of the time, | ⟩ 1 the other 10% | ⟩ | ⟩ It’s | ⟩ 0 It’s | ⟩ 1

Slide 58

Slide 58 text

Slide 59

Slide 59 text

Slide 60

Slide 60 text

Slide 61

Slide 61 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Why are qubits useful? • Qubit express perfect randomness • Some problems can be solved exponentially faster • New algorithms are possible! For example, • Grover’s search algorithm – O( ) instead of O() • Shor’s factoring algorithm – runs in polynomial time • Possible applications are: • Molecular simulation • Optimization • Quantum Machine Learning

Slide 62

Slide 62 text

Slide 63

Slide 63 text

Slide 64

Slide 64 text

Slide 65

Slide 65 text

Slide 66

Slide 66 text

Slide 67

Slide 67 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Explore and experiment with quantum computing using Amazon Braket, it’s easier than what you may think! Amazon Braket Gate-based superconducting qubits Gate-based ion traps Quantum annealing AWS Simulator Notebook

Slide 68

Slide 68 text

© 2020, Amazon Web Services, Inc. or its Affiliates. Quantum Demo J Grover’s search algorithm – O( ) instead of O() For simplicity, let’s search between 8 different values: 111 000 001 010 011 100 101 110 111