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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

AWS Braket: The Quantum Computing Tool

Gitesh Dhore
Gitesh Dhore Last Updated : 12 Oct, 2022
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

AWS named the service after a standard quantum notation called Braket, created in the late 1930s by Paul Dirac, a famous theoretical physicist. The Braket is a fully managed AWS service that provides a primarily technology-agnostic environment. Customers can design, develop, test, and run their quantum algorithms. Braket also offers simulated quantum computers that use Amazon EC2 computing resources and can simulate up to 34 qubits for testing and troubleshooting algorithms.

AWS Bracket
https://aws.amazon.com

What is AWS Braket?

Each piece of quantum hardware is accessible through Braket but resides on the premises of each respective provider. Once the customer is satisfied that their quantum algorithms are complete, they can run them on three different types of quantum hardware of their choice. Amazon Braket quantum hardware providers are: Superconducting qubits are an advanced technology with easy coupling, electronic control, and fast gates. It is the most widely used quantum technology.

IonQ provides an 11-qubit gated ion quantum computer. Individual ions, held in a vacuum by an electromagnetic trap generated by surrounding electrodes, use laser pulses to perform gate operations. Note: You cannot compare qubit-to-qubit between quantum annealers and gate-based machines. They are different technologies.

Prices of AWS Braket

Braket’s pricing structure has a task fee and a shot fee. You can also pay an hourly rate for using the quantum simulator managed by Amazon Braket. Amazon Braket defines a job as repeated shots (runs) based on the same circuit design or annealing problem. There is a per-task pricing tier for D-Wave, Rigetti, and IonQ. Missiles have a variable cost. Think of a shot as every time the algorithm runs. D-Wave, Rigetti, and IonQ have different speeds per shot.

Working and Functionalities of Braket

Amazon Braket is not the only quantum cloud service. IBM has a huge fleet of 22 superconducting quantum computers. In 2016, IBM was the first company to bring a quantum computer online. In addition to offering premium services, most of IBM’s quantum systems are publicly available, along with complete tutorials and a large library of useful algorithms. According to IBM, billions of quantum circuits are running on its network monthly.

Microsoft Azure Quantum offers a similar cloud service to Braket. However, its platform is still in a version for a select group of customers. Not available for general use. The Azure platform features trapped ion technologies from Honeywell Quantum Solutions and IonQ plus superconducting hardware from Quantum Circuits, Inc. (QCI). Honeywell recently announced that it has the world’s most powerful quantum computer with a quantum volume of 64.

Although the quality of the qubits is an important factor, the number of qubits alone does not tell the whole performance story. Meanwhile, quantum volume is a suitable measurement for gate-based quantum computers. It is hardware agnostic and considers the number of qubits, connectivity, ty and gate, and measurement errors. Additionally, and no less important, its numerical simplicity makes quantum computing more understandable to the general public, corporate management, and investors. Unfortunately, although it would be interesting, few companies publish their quantum volume numbers.

AWS Bracket
https://www.forbes.com

Keep in mind that we are still in the experimental era of quantum computing. A classical computer can usually do everything a quantum computer can do. Nevertheless, quantum cloud computing provides widespread global access to this technology. It enables customers, students, and researchers to start learning and planning for the future. With some of the technologies we’ve seen in the pipeline, there will come a time, maybe in 10 to 20 years, when quantum applications will be an integral part of our daily lives. Then quantum computers will be able to solve tomorrow’s big scientific, financial, and medical problems.

Recently, Google also made a breakthrough when it announced that the company had achieved quantum supremacy. The researchers published a scientific journal discussing efforts and techniques to build a quantum computer that can perform tasks that no classical computer can. On the other hand, instead of making against a big thing, the e-commerce giant Amazon quietly laid its cards on the table. Realizing the potential to solve computational problems beyond the reach of classical computers by using the laws of quantum mechanics to build more powerful information processing tools, the electronic communications giant’s cloud computing platform has joined hands with D-Wave, IonQ, and Rigetti to introduce its all-new quantum service of computers known as Braket.

A few days ago, the AWS office finally announced the launch of a trial version of Braket, its first-ever Quan computing service. The braket is derived from “braket notation,” a common for quantum states in quantum mechanics. Amazon’s Braket is a fully managed service that helps start quantum computing by providing a development environment for researching and designing quantum algorithms, testing them on simulated quantum computers, and running them on various quantum hardware technologies.

Why Use a Braket?

Accessing quantum computing hardware to run algorithms and optimize designs can be expensive and inconvenient. Also, programming quantum computers to solve problems requires new skills. Amazon Braket helps overcome these difficulties by providing a service that allows developers, researchers, and scientists to explore, evaluate, and experiment with quantum computing.

The service allows users to choose from a range of quantum computers, which include gate-based superconducting computers from Rigetti, superconducting quantum annealing computers from D-Wave, and ion trap computers from IonQ. Braket also allows users to design their quantum algorithms from scratch or choose from pre-built algorithms. Once an algorithm is defined, Braket provides a fully managed simulation service to assist in troubleshooting and verifying the implementation. The user can then run the algorithm on quantum computers, as mentioned earlier.

Conclusion

To make it easier for users to develop a hybrid algorithm that combines classical and quantum tasks, Amazon Braket helps manage classical computing resources and establish low-latency connections to quantum hardware.

  • Although the quality of the qubits is an important factor, the number of qubits alone does not tell the whole performance story. Meanwhile, quantum volume is a suitable measurement for gate-based quantum computers.
  • The service allows users to choose from a range of quantum computers, which include gate-based superconducting computers from Rigetti, superconducting quantum annealing computers from D-Wave, and ion trap computers from IonQ.
  • A classical computer can usually do everything a quantum computer can do. Nevertheless, quantum cloud computing provides widespread global access to this technology. It enables customers, students, and researchers to start learning and planning for the future.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 

AWSAWS Braketblogathonquantum computing
Gitesh Dhore
Gitesh Dhore

I am a Machine Learning Enthusiast. Done some Industry level projects on Data Science and Machine Learning. Have Certifications in Python and ML from trusted sources like data camp and Skills vertex. My Goal in life is to perceive a career in Data Industry.

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