Usage of quantum computing to enhance machine learning

8 min readJan 9, 2022

Over the past few years, there has been a steady increase in data and power consolidation over time, the only data responsible for the current era of technology to improve technically driven technologies. Therefore, many data professionals use this excess amount of data to turn comprehension into action, and in order to analyze important data and interpretations, machine learning algorithms have achieved success. They have been used successfully in many fields from computer vision and gaming to business decision-making and forecasting.

However, we will soon come to the point where current calculating tools will not be enough to manage this continuously generated data. There are many hardware-based simulation solutions available, such as Graphics Unit (GPUs) and Tensor Processing Units that can greatly improve speed but can also provide some structured solutions.

Today, with computational data growing so rapidly over time, the Classic Machine Learning (ML) algorithms are not capable of extracting all information within sound, complex, and informal data. These growing databases encourage researchers to explore quantum computing opportunities in faster machine learning algorithms. (Read the basics of machine learning by link)

At the same time, we will discuss the launch of Quantum Machine Learning, what Quantum Computing is and the potential technologies for this blog. Finally, we will learn how quantum computing is useful for machine learning with specific cases used.

Introduction of Quantum Machine Learning

The main problems with classical machine learning algorithms are those built back in the 1950s and late 1990s, they did not match the current complex database as described above. For example, a common type of sensory networks that were actually developed were established in the late 1950s, and back in the 1980s with a lot of development and stopped working properly.

“The purpose of quantum computing based compassionate artificial intelligence is to develop integrated systems that can preserve and enhance human values of peace, love, happiness, and freedom.”― Amit Ray

But now, we have computers that are faster and millions of times, data with billions of times, it is not possible for all algorithms to work correctly in the current situation. However, we have large-scale data that grows very rapidly with limited integration capabilities, and as a result, it is difficult to extract plausible meaning from this large data and that is where quantum calculation applies. (As you begin with quantum computation, read the amazing story of quantum elevation achieved by Google)

Within this framework, the emergence of quantum machine learning complements the expectations of many industries and organizations to a large extent where the continued growth of these industries depends entirely on the data from which they are produced. (You do not want to learn how to manage big data in 5 easy steps of quality analysis).

As the name suggests, Quantum Machine Learning (QML) is a combination of quantum computing and Advanced Machine Learning. In other words, QML uses quantum computing power to process information faster than a standard computer.
It focuses primarily on providing synthesis that defines machine learning algorithms that are critical to quantum framework. (Now, referring to Decision Tree, another machine learning algorithm)
Quantum machine learning also opens the door for researchers to explore structural similarities between physical systems or learning systems, particularly in sensory networks. (Related Blog: Keras Lesson: Neural Network Library for In-Depth Reading)
Many mathematical and numerical techniques from quantum physics can also be used in deep learning algorithms and vice Versa.

Let’s take a look at the basic introduction to Quantum Computers, Quantum Computing, and how its potential technologies improve machine learning.

What are Quantum Computers?

Quantum computers are machines that use the basic features of quantum physics to reproduce data and perform large calculations. This is considered to be a significantly lucrative business for some activities where they can perform much better than larger computers.

For example, older computers, including smartphones and laptops, encoded each piece of information in binary “bits” (in the 0’s and 1’s) form. but, in a quantum computer, the first memory unit is a quantum bit or qubit.

To clarify further, the Qubits are designed with portable systems, such as electron rotation or photon shape. However, such systems can be in a different configuration, even simultaneously, according to the quantum superposition structure. Also, Qubits are also fully integrated using an object, known as quantum entanglement. As a result, a series of qubits can display several objects at once. (From)

Consider for example, eight bits is enough for an old computer to display any number between 0 and 255. But again, eight qubits is enough, on a quantum computer, to define each number between 0 and 255 at the same time. Also, hundreds of cohesive qubits are enough to provide extra numbers compared to the atoms in the universe. And that is where quantum computers mark their limit over older computers. In a situation where there is a large number of possible combinations, quantum computers may calculate them simultaneously.

Quantum computers can promote the growth and development of;

New scientific discoveries, Lifesaving drugs,
Mechanical learning methods and diagnostic methods in advance,
Equipment for making the most reliable devices and efficient structures,
Financial strategies for a better life after retirement, as well
Algorithms for quickly converting resources, etc.

At present, quantum computers are highly sensitive: heat, electromagnetic fields and collisions with air molecules may strike qubit with the loss of their quantum properties. This process, known as quantum decoherence, causes the system to crash. Therefore, quantum computers need to protect the qubits from external disturbances, by literally removing them, keeping them cool or finishing them carefully with a controlled power rotation.

What is Quantum Computing?

“Quantum computing is the study of a non-classical model of computation”.

Unlike traditional models that rely on the ancient practices of computer complexity, quantum calculation may transform memory into a quantum superposition of ancient states that could exist.

“Quantum computing uses the remarkable laws of quantum mechanics to extract information. It focuses on studying the problem of storage, processing, converting information into quantum mechanical systems.”

Quantum computing is a new invention that will take AI to the next level in this data-generating world. QML applications can range from breaking cryptography systems to developing new drugs.

Traditional computers use long “bit bits” (including 0 and 1), and on the other hand, quantum computers use quantum bits or qubits. Quantum computers perform calculations based on the probability of the object being measured before, instead of 0’s and 1’s — meaning they have greater capacity to process additional data.

Qubits is a quantum system that integrates standard codes (0 and 1) into two separate regions. Because of these qubits, a certain number of difficult tasks that are considered difficult to solve by ancient computers can be solved easily and efficiently by quantum computers.

Quantum computers can promote the growth and development of;

New scientific discoveries, Lifesaving drugs, Mechanical learning methods, and diagnostic methods in advance,
Equipment for making the most reliable devices and efficient structures,
Financial strategies for a better life after retirement, as well
Algorithms for quickly converting resources, etc.

What are the Potential technologies of Quantum Computing?

1. Cryptography

Cryptography is a way to protect any confidential or sensitive information by using codes and tokens, so only confidential information will be obtained and retrieved by the intended person.

It is about building and analyzing agreements to prevent the public or other organizations.
In cryptographic systems for public key, integer factorization is not possible for any computer common to large integers if it is a product of a few key numbers.
On the other hand, quantum computing can easily solve this problem using the Shore’s factorization algorithm.
Quantum cryptography is much more secure than traditional cryptography systems against quantum robberies.

2. Quantum Supremacy

In our blog, what does google’s quantum supremacy mean? We explained that Google claims to reach the Quantum Height being challenged by IBM, —

Quantum computers are still being developed but are at the right end of modern quantum technology.
The purpose of Quantum supremacy is that “a quantum compact device can solve a problem that traditional computers cannot”.
Boson sampling is a non-universal quantum computer that is very specific to build any universal quantum computer to date, and is a proposed test to achieve quantum computational quantity.

3. Quantum Simulation

Numerical simulation of quantum systems is difficult to understand in its natural phenomena.

In many fields such as superconducting materials, quantum chemistry, nanotechnology, etc. it is thought to be defined by models that can be adequately solved by any ancient computer.

Using quantum computation to solve such quantum simulations is one of the most important applications in the field of quantum computing.

4. Quantum Search

The most popular example of quantum data search can be solved with Grover’s algorithm using fewer queries than those required by older algorithms.

Searching on the Quantum website of Grover’s algorithm accomplishes the task of finding the target object on an unfiltered site at a much faster time than the old computer.

Quantum search applications are of great interest to Government agencies where they have billions of data on Quantum computing data.

How is Quantum Computing useful for Machine Learning???

Every two seconds, the sensors that measure the United States grid generate 3 petabytes of data (about 3 million gigabytes). Data analysis on that scale where important information is hidden on this inaccessible website.

In this blog, you probably got the impression that quantum computing has the ability to make learning machine and AI faster faster than its traditional counterparts.

Let’s look at some specific areas where quantum computing can help:

Quantum annealers and job loss reduction

Basically quantum annealers are part or different versions of quantum computers that specialize in locating minimal or near-global minima speculation than a standard computer. Total quantum structures such as a tunnel can save a considerable amount of access cost settings from local minima to global minima.

Augmenting Support Vector Machine for size reduction:

If data points on any data set are displayed in high magnitude, it is difficult for any older system to calculate the calculation of such magnitude. Thus, with a quantum computer, we can solve even the most complex or the most complex databases. We call this the algorithm Support Vector Machine (SVM) Quantum Kernel Algorithm.

Conclusion

In this blog, we learned that quantum computer literacy and quantum machine learning are the next big thing in Machine Learning and artificial intelligence. We have described the power of quantum computing in the four major applications on this blog, as these four applications are so extensive that these cannot be described here properly. They want their specific meaning and blog.
Sharing is Care! If you found this blog useful, please share it with your friends and data science lovers. And also Don’t let the big, best of you.

Writers:

Diya Palresha

Sahil Adhav

Ayush Prasad

Aryan Aher