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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

Is Quantum Computing the Future of Artificial Intelligence?

Mrinal Singh
Mrinal Singh Last Updated : 10 May, 2022
5 min read

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

quantum computing for artificial intelligence

Source: Forbes.com

Introduction

It is not hidden from the audience that quantum computing is the future of data processing. Tech giants like IBM, Google, and Microsoft are all aggressively pursuing quantum computing technology for a good reason. The massive speedups and power savings of quantum computers will redefine what is possible in artificial intelligence and machine learning fields.
However, there is a disturbing lack of discussion around the implications of quantum computing for artificial intelligence. This article will explore the implications of quantum computing for artificial intelligence and why more people should be talking about it.

What is Quantum Computing?

Quantum computing is a style of computing that relies on the principles of quantum mechanics to function. In classical computing, data is encoded in bits that can be either 1 or 0.

On the other hand, Quantum computing uses qubits that can be both 1 and 0 simultaneously. This allows many calculations to be done simultaneously, so quantum computing is powerful. It’s also why it’s seen as the future of artificial intelligence and data science.

How is Quantum Computing Different from Classical Computing?

The essential dissimilarity between classical and quantum computing is that while classical computers use either 1 or 0, quantum computers use qubits, both 1 and 0 simultaneously. Quantum computing takes advantage of the features of quantum mechanics to process information using qubits.

These can represent a combination of 1 and 0, allowing for many calculations to be done simultaneously. Quantum computers are not subject to the same errors as classical computers due to their use of qubits. This makes them more reliable for sensitive applications, like those used in artificial intelligence.

This allows them to explore many different solutions to a problem simultaneously, so quantum computers are powerful for simulating complex systems and machine learning tasks.

What are the Advantages of Quantum Computing?

There are many advantages that quantum computing has over classical computing. One of the critical advantages of quantum computing is that it can solve problems much faster than classical computers.

This is because a quantum computer harnesses the features of both a particle and a wave, which allows it to explore many different solutions at once, which is called “quantum parallelism.”.

Additionally, quantum computers are not affected by noise and can function in extreme conditions, perfect for AI and machine learning applications. Another advantage is that quantum computers can overcome errors in traditional computing systems.

Finally, quantum computers can store and process large amounts of data, which is essential for artificial intelligence and machine learning models.

Drawbacks of Quantum Computing

Besides all its benefits and advantages, people are afraid to try quantum computing, and the reasons are valid. Due to its robust nature, it is exceedingly hard to engineer, build and program a quantum computer. It is no wonder how difficult it will be to train an AI model to detect objects in an image.

Due to its complex nature, quantum computers and programs are crippled by errors in noise, faults, and loss of quantum coherence. And as the number of qubits multiplies, the isolation of these qubits from their environment becomes extremely difficult to sustain, and decoherence is bound to happen that is bringing in a bunch of errors.

Hence, the characteristics that make quantum systems powerful also make them delicate and cause the whole system to fall apart.

Why is Quantum Computing the Future of Artificial Intelligence?

quantum computing for artificial intelligence

Source: Psychology.com

In classical computing, bits are either one or zero. However, a quantum bit or qubit can be simultaneously one and zero in quantum computing, which opens up a whole new realm of possibilities for computing power.

Quantum computers can crack problems much quicker than classical computers because they can try several solutions simultaneously. They’re also not constrained by the same limitations as classical computers, meaning they can solve currently impossible problems.

This makes quantum computing the perfect candidate for powering artificial intelligence. The vast amounts of data processed by AI systems require enormous computational power. Quantum computers have the potential to provide that power and thus enable AI to reach its full potential.

Real-life Applications

This section lets us see some real-life practical applications where quantum computing in AI can change the future.

1. Financial Services & Healthcare: This is one field where quantum computing, with the help of AI, is helping solve many complexities with its speed and specificity.

2. Solving Mathematical Problems: Many types of research are going on in maths, science, and history. With the help of quantum computers, it can be solved in a much lower period. For example, if a classical computer takes 10 years to solve a mathematical problem, quantum computers, with the help of AI, can solve that in less than a month.

3. Fraud Detection & CyberSecurity: With the help of AI algorithms, Quantum computers are helping agencies enhance their protection and increase the security on the internet.

Why is Quantum Computing still Unknown to Many?

                    Source: BostonGlobe.com

There are a few possible explanations for why quantum computing isn’t being discussed more in the AI and ML communities. One possibility is that people are unsure how to apply quantum computing to their work. Another possibility is that the potential applications of quantum computing are so vast and far-reaching that people are hesitant to get too excited about it until there’s a more concrete plan for how it will be used.

Conclusion

Quantum computing is a technology that is quickly gaining traction due to its potential applications in various industries. Despite this, there is a lack of chitchat around quantum computing and its role in the future of artificial intelligence.

Quantum computers can decode problems much faster than classical computers and can be used to model large-scale systems and molecules. They can also handle large amounts of data, which is essential for training artificial intelligence models. As quantum computing becomes more accessible, it will play a vital role in developing artificial intelligence and future applications.

Hope you enjoyed reading my article. If you would like to discuss this further, please comment below. Will be happy to have a fruitful discussion on it.

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

Artificial IntelligenceblogathonClassical Computingquantum computing
Mrinal Singh
Mrinal Singh

Data Scientist and a Technical Writer! I will give you the best of Open-Source and AI.

Talks about #chatgpt, #opensource, #contentcreation, #communitybuilding, and #artificialintelligence

Technical Writer | Data Science, ML, AI, Open-Source | Do More with Data - Litmus

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Responses From Readers

Clear

Annna
Annna

Interesting post, thanks for sharing.

123

N Arvind
N Arvind

Very nice article Mrinal. Congratulations! I had read that scientists have already applied quantum computing to Computer vision successfully.

123

Joline Mccleary
Joline Mccleary

It's rare to find such a thorough and engaging exploration of this topic. pcsite.co.uk

123

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The Role of Quantum Computing in Advancing Artificial Intelligence
The Role of Quantum Computing in Advancing Artificial Intelligence
Applications of Quantum Computing
Applications of Quantum Computing

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