How Does Quantum Computing Improve Machine Learning?

  • Dinesh Kumawat
  • Dec 02, 2019
  • Machine Learning
  • Updated on: Oct 10, 2020
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Over the last few years, there is a continuous increment in data and computational power with time, only the data is responsible in the current technological era to upgrade in tech-driven strategies. Therefore, many data experts are exploiting this excess amount of data in order to convert insight into actions, and for valuable data analysis and interpretation, machine learning algorithms have achieved the accomplishment. They have been successfully used in numerous fields inducting from computer vision and playing games to making business decisions and forecasting predictions. 


However, soon we will reach a point where current computational tools will not suffice to handle this continuously generated data. There are many hardware-based simulated solutions available, such as Graphics Processing Units (GPUs) and Tensor Processing Units that can greatly improve speed but also not able to provide some structured solutions. 


Nowadays, with the computational data that increases exponentially with time, classical Machine Learning (ML) algorithms are not appropriate to extract all the information inside the noisy, complex, and unstructured data. These growing datasets are prompting researchers to explore the possibilities of quantum computing for rapid machine learning algorithms. (Read the basics of machine learning through the link)


On the same note, we will discuss the introduction of Quantum Machine Learning, what Quantum Computing is and its potential technologies through this blog. Towards the end, we will learn how quantum computing is useful for machine learning with some used cases.



Introduction of Quantum Machine Learning


The primary problems in classical machine learning algorithms are among them that were developed back in the year 1950 to late 1990, they didn’t fit to existing current complicated dataset quite well as described above. For example, a common form of artificial neural networks which are actually invented in late 1950, and back in the year 1980 with too many advances and then they 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 superfast and millions of times powerful computers, the data which is trillions of times more in amount, it's not possible for all the algorithms to work accurately in the current scenario. Yet, we have data on a large scale that is growing very rapidly and limited computational power, as a consequence, it is difficult to extract meaningful inference from this huge data and this is where quantum computation works. (As initiating with quantum computation, read the amazing story of quantum supremacy  that Google has achieved)     


Within this framework, the emergence of quantum machine learning fulfils the hopes of many industries and organizations at a huge level where continuous growth of these industries fully depends on data generated through them. (Don’t you want to learn how to handle huge data in 5 easy steps for qualitative analysis).  


  1. As the name suggests, Quantum Machine Learning (QML) is a mixture of quantum computing and advanced Machine Learning. In other words, QML leverages the power of quantum computing to process information faster than traditional computing. 

  2. It is primarily focused on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. (Now, referring the Decision Tree, another machine learning algorithm)

  3. Quantum machine learning also opens the gate for researchers to explore the structural similarities between the physical systems or a learning system, mainly in the neural networks. (Related blog: Keras tutorial: A Neural Network Library in Deep Learning)

  4. There are many mathematical and numerical techniques from quantum physics that can also be applied in deep learning algorithms and vice Versa. 


Let’s learn the basic introduction of Quantum Computers, Quantum Computing, and how its potential technologies boost up machine learning.



What are Quantum Computers?


Quantum computers are machines that adopt the fundamental properties of quantum physics in order to reposit data and perform major computations. This is considered as a notably advantageous entity for some specific tasks where they could hugely outperform supercomputers.


For instance, classical computers, that involve smartphones and laptops, encode each piece of information in binary “bits” (in the form of 0 and 1). but, in a quantum computer, the elementary unit of memory is a quantum bit or qubit. 


If we specify more in details, Qubits are designed through physical systems, such as the spin of an electron or the orientation of a photon. However, such systems could be in distinct configuration, even all at once, as according to the property of quantum superposition. Also, Qubits are also completely connected together via a phenomenon,  known as quantum entanglement. As a consequence, a series of qubits may reflect several things simultaneously. (From)


Consider the example, eight bits is far enough for a classical computer to display any number in between 0 and 255. But also, eight qubits is enough, for a quantum computer, to describe every number amid 0 and 255 at the same time.  Also, hundreds of entangled qubits are enough to render more numbers in comparison to the atoms present in the universe. And this is where quantum computers mark their edge over classical computers. In the situation where there are a massive number of possible combinations, quantum computers may account them simultaneously. 


Quantum computers could impel the growth and development of;

  • New findings in science, 

  • Medications in order to save lives, 

  • Machine learning methods and approaches to diagnose illnesses at an earlier stage, 

  • Materials for making more reliable devices and efficient structures, 

  • Financial strategies for better living after retirement, and 

  • Algorithms to instantly converge resources, etc.

For now, quantum computers are profoundly sensitive: heat, electromagnetic fields and collisions with air molecules could hit a qubit for losing their quantum properties. This process, known as quantum decoherence, makes the system to crash. Therefore, quantum computers demand to shield qubits from external interference, either by physically detaching them, keeping them cool or exterminating them carefully with regulated oscillations of energy.



What is Quantum Computing?


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


Unlike traditional models that rely on classical representations of computational complexity, the quantum computation could transform the memory into a quantum superposition of possible classical states. 


“Quantum computing uses the amazing laws of quantum mechanics to extract information. It focuses on studying the problem of storing, processing, transforming information into quantum mechanical systems.” Quantum computing is the new innovation that will take AI to the next level in this data generating world. QML applications can range from the breaking of cryptographic systems to composing new medicines. 


Traditional computers use long strings of “bits” (which encode 0 and 1), on the other hand, a quantum computer uses quantum bits or qubits. Quantum computers perform calculations based on the probability of an object’s state before it is measured, instead of just 0’s and 1’s - which means they have huge potential to process more amounts of data.

Qubits are a quantum system that encodes conventional encoders (0 and 1) into two separate states. Because of these qubits, a certain number of difficult tasks which are considered as hard to solve by classical computers can be solved easily and efficiently by quantum computers. You can read more about quantum computing in this article.


Quantum computers could impel the growth and development of;

  • New findings in science, 

  • Medications in order to save lives, 

  • Machine learning methods and approaches to diagnose illnesses at an earlier stage, 

  • Materials for making more reliable devices and efficient structures, 

  • Financial strategies for better living after retirement, and 

  • Algorithms to instantly converge resources, etc.


What are the Potential technologies of Quantum Computing?


1. Cryptography


Cryptography is a method to protect any private or sensitive information through the usage of codes and tokens, so only confidential information would be received and decoded by the intended person. 


  1. It is also about constructing and analyzing protocols to prevent it from the public or other parties.

  2. In public-key cryptographic systems, integer factorization is infeasible for any ordinary computer for large integers if they are the product of a few prime numbers. 

  3. On the other side, a quantum computer can easily solve this problem using Shore’s factorization algorithm. 

  4. Quantum cryptography is more secure than the traditional cryptography systems against quantum hacking.



2. Quantum Supremacy


In our blog, what does google’s quantum supremacy describe? we have described that Google claims to achieve Quantum Supremacy that gets challenged by IBM, Quantum computers are still under development but are at the correct edge in today's quantum technologies.


  1. Quantum supremacy’s goal is that “a programmable quantum device can solve a problem that classical traditional computers practically cannot”. 
  2. Boson sampling is a non-universal quantum computer that is significantly more straightforward to build any universal quantum computer so far, it is also a proposed experiment to achieve quantum computational supremacy.

The image represents major potential technologies of Quantum Computing, these technologies are enhancing the performance of Machine Learning algorithms and attain their superability.

List of some potential technologies of Quantum computing

3. Quantum Simulation


The numerical simulation of quantum systems is difficult to understand its natural phenomena. 


In many areas such as superconducting materials, quantum chemistry, nanotechnology, etc are thought to be described by models that can not be solved sufficiently by any classical computers. 

Using quantum computation to solve such quantum simulations are one of the key applications in the field of quantum computing.



4. Quantum Search


The most popular example of a quantum database search can be solved by Grover’s algorithm using fewer queries than that are required by classical algorithms. 


Quantum database search of Grover’s algorithm accomplishes the task of finding the target element in an unsorted database in a time that is quadratically faster than a classical computer.

Quantum search applications are a major interest of Government agencies where they have trillions of data in the amount in Quantum computing. (Related blog: How Artificial Intelligence (AI) can be used in Politics & Government?)



How is Quantum Computing useful for Machine Learning?


Every two seconds, sensors measuring the United States’ electrical grid collects 3 petabytes of data (nearly 3 million gigabytes). Data analysis on that scale when important information is hidden in this inaccessible database.


In this blog, you have probably gained an idea of how quantum computing has the potential to make machine learning and AI speed faster compared to their traditional counterparts. 



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


  1. Quantum annealers and minimization of loss function: 


Basically quantum annealers are part or different versions of quantum computers are specialize in finding local minima or closer approximation of global minima than a traditional computer. Overall quantum properties such as tunneling can save large energy of cost settings to reach from local minima to global minima.


  1. Augmenting Support Vector Machine for dimensionality reduction: 


When data points in any dataset are projected in higher dimensions, it is hard for any classical system to compute such higher dimension computations. So, with a quantum computer, we can solve even the most complex or higher dimensional dataset computations. We call this algorithm Support Vector Machine(SVM)  Quantum Kernel Algorithm.





In this blog, we have learned that quantum computing and quantum machine learning is the next big thing in Machine Learning and Artificial Intelligence. We have described quantum computing potential in major four applications in this blog, as these four applications are very broad that these can not be described here properly. They demand their particular explanation and blog. 


In the future, we come up with all the blogs related to quantum machine learning and quantum computing. Also Sharing is Caring! If you found this blog helpful, please share it with your friends and data science enthusiasts. And also Don’t let the great, best of you.