Introduction
Machine learning is one of the trending topics in the current industry and business scenarios, where almost all companies and businesses want to integrate machine learning applications into their working mechanisms and work environments. It is growing daily, and its application is enhancing time by time, where many scientists and students are working on the research of the same.
Quantum computing has been a hot topic recently and has more comprehensive applications than regular classical computers. This article will discuss quantum machine learning, which integrates quantum computing and machine learning. We will discuss its working, application, and some of the advantages and disadvantages associated with it. This article will help one to understand the new topic and will help to plan and learn accordingly.
Before directly jumping to the concept, let us discuss quantum computing first.
Learning Objectives
This article will help one to:
1. To understand the core idea behind quantum computing
2. Difference between the three types
3. How it works?
4. Applications of the machine learning
5. Answer interview questions related to quantum machine learning
This article was published as a part of the Data Science Blogathon.
Table of Contents
What is Quantum Computing?
Quantum computing is a branch of computer science that uses the quantum physics concepts to solve problems and statements that regular or classical computers can’t solve. Like the bits in the classical computers, the quantum computer uses qubits capable of solving complex problems and passing the information to each other.
Here the qubits are very advanced compared to the standard bits of classical computers and can store data. Also, the qubits can be imagined as a particular wave in quantum physics that holds the information and the data and can pass it.
It is better to compare classical and quantum machine learning to get a clear idea. However, before comparing them, let us compare normal programming and machine learning.
Types of Quantum Machine Learning
Quantum machine learning combines quantum algorithms and machine learning techniques to improve the speed and accuracy of learning from data. It uses quantum physics and quantum computers to enhance the capabilities of machine learning. There are different types of quantum machine learning, such as:
- Quantum Neural Networks (QNN): These are neural networks that operate on quantum logic gates and qubits, which are the basic units of quantum information. QNNs can take advantage of quantum phenomena such as superposition and entanglement, which allow qubits to exist in multiple states and be correlated with each other. QNNs can perform faster and more efficient learning than classical neural networks.
- Quantum Support Vector Machines (QSVM): These are support vector machines that use quantum algorithms to find the optimal hyperplane that separates the data points into different classes. QSVMs can use quantum speedup and quantum parallelism, which enable quantum computers to perform multiple operations simultaneously and solve complex problems faster than classical computers.
- Quantum Reinforcement Learning (QRL): These are reinforcement learning algorithms that use quantum agents and quantum environments to learn optimal policies and actions. QRL can use quantum resources and quantum feedback, which provide quantum agents with more information and better rewards than classical agents.
Applications of Quantum Computing
1. More Faster Algorithms
With the help of quantum computing, we can make the algorithm’s training on more enormous datasets faster and more efficient. The QML algorithms can be super faster compared to classical machine learning where qubits will play major role.
2. Solving Complex Data Patterns
Quantum computing can help solve complicated data patterns that can not be solved by classical machine learning and deep learning algorithms. Very complicated datasets where data correlations and patterns are not recognisable and solvable, the QML can make it possible.
3. Develop Advanced Algorithms
Quantum computing, with the integration of machine learning can help build and design more advanced machine learning algorithms. Advanced algorithms integrated with quantum computing can solve more problems in less times with greater accuracies.
4. Advancement in Reinforcement Learning
Reinforcement learning can be developed more with the help of quantum computing concepts. Some novel concepts of the reinforcement learning can be developed and enhanced by qunits based quantum machine learning.
5. Advanced Computer Vision
Quantum machine learning can also help advance the computer vision application and make the existing deep learning algorithms quicker and more efficient. With the help of quantum machine learning, we can develop more advanced and accurate image segmentation and processing applications.
Classical Programs vs. Machine Learning
As we might think that machine learning is the same as classical programming, it is wrong. In classical programming, the input is taken, preprocessed, and then directed to the output is given. Here we have pre-defined functions and rules where we take the information, apply functions and regulations to it, and whatever result we get, we display it as an output of the program.
Machine learning is very different from classical programming, where the input is first divided into two parts, training and testing. Then the training data is fed to the machine learning program where no pre-defined function exists in the rules. The model learns from the training data by itself, and once it is trained, the model is tested on actual values of the testing data, where the loss function helps to determine the error and mistakes our model is making.
As we saw that machine learning is entirely different than the classical programming, now let us compare.
Machine Learning vs. Quantum Machine Learning
As we discussed that machine learning is a set of program that takes the data as input, preprocess it, and learn from it. Then, the model is tested through the testing data, and the loss of the model is calculated.
We take the ideas of atoms and molecules from quantum physics and train the model on training data rather than the bits level. In quantum machine learning, qubits are used, which are very advanced from the regular bits. We also test the model on the testing data after its training, calculate the loss function, and try to minimize the same.
What Exactly is Quantum Machine Learning?
Now as we have an idea about classical programming, machine learning, and quantum computing, it is the best time to learn about QML. It can be defined as the integration of classical machine learning and quantum computing where the machine learning concepts and techniques are applied with the help of quantum computing rather than classical computing and programs.
As the standard or classical computer works with bits with either the value 0 or 1, the same quantum computers have qubits or quantum bits, which also store the data. Still, it leverages the principle of superposition, which means that, unlike the classical bits, the qubit can be in multiple states simultaneously.
To understand the principle of superposition, let us take an example. We have a coin. We toss it, and now it can give us a tail or a head. Before launching the coss or while the coin is flipping in the air, we can’t say it’s in a heads or tails state. That means it is partially in heads and tails, according to the person’s view. The principle here is called the superposition of the coin, where the coin can be in multiple states simultaneously.
Conclusion
This article discussed quantum machine learning and how it differs from classical programming and machine learning. This article will help one understand the concept, its core idea, and how it can be helpful in the future.
Some of the key takeaways are as follows:
- In classical programming, we just take the input, process it and display the output with the help of pre-defined functions.
- Machine learning is an advanced approach to classical programming where the model learns from the data and results from an output.
- QML is an approach that uses machine learning with quantum computing concepts where the qubits are used rather than bits, and the model is trained.
- QML can help in making the existing algorithms faster, developing new algorithms, and solving more complex algorithms.
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