Quantum Computing – A Tech Story

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Quantum Computing – A Tech Story
Dr. Selvanayaki Kolandapalayam Shanmugam, Prof. J. Shyamala Devi, Dr. R. Senthil Kumar
Department of Mathematics and computer Science, Ashland University, Ashland, Ohio, USA
SRM Institute of Science and Technology, Ramapuram, Chennai, Tamilnadu, India
Department of Computer Science with Cognitive Systems, Dr. N.G.P Arts and Science College, Coimbatore, Tamil Nadu,
India.
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Abstract
The growth in technology is endless and touched the base of Quantum computing and justifies overcoming the limitations
encountered in the field of simulation, optimization, and machine learning problems and several domains of modern
technologies. This article will review the growth of computing, Machine Learning, the basic building blocks of quantum
computing and its related concepts, Quantum Machine Learning, and focused applications. The real-time applications of
Quantum computing in various fields are summarized to get an insight into its benefits. Despite the tremendous growth,
scientific and engineering challenges are seen in the development of quantum computers and computing by considering the
application of technology to applications in real-time. Quantum Computing provides supervised and unsupervised machine
learning algorithms with the speed of training and computational power at less price, and it is a great gift to the Artificial
Intelligence (AI) and Machine Learning Community.
Keywords: Machine Learning, Quantum Computing, Computations, Supervised and unsupervised algorithms
1. Introduction
Computing is using computer technology to do or finish a given task. Computing has less power than Quantum computing.
Quantum computing solves complex problems that normal computing cannot do. Quantum computing works under a set of
conditions because of its quantum mechanics. Computing is a purposeful activity requiring and benefiting from computing
machinery. Computing includes the study and experiment of algorithmic processes and the development of both hardware and
software. Computing has distinct aspects:scientific, engineering, mathematical, technological, and social. Computing is used for
designing virtual products, video conferences, and interior design.
1.1 Growth of Computing
The Co-founder of Intel, Gordon E. Moore states the Moore’s Law in 1965 as the number of transistors on computer chips
doubles every two years. In 1975, it was proved to be accurate and added as, “in addition to computing power doubling every
two years, the cost of that computing power would be cut in half every two years”. This is a justification for how technology is
changing with an observation of the long-term trend. The exponential growth is witnessed due to the rapid increase in
computing capabilities, the computational capacity of computers has increased exponentially doubling every 1.5 years from
1975 to 2009. Later, an increase and exponential growth is seen in supercomputer power. It is approximately 50-60 times
faster than Moore’s law discussed above. With the growth in technology and computing devices, the world is ruled by Artificial
intelligence, and it has given an increase of 300,000 folds between 2012 and 2018. Since then, technology has its growth
towards AI and the same way is Computing. Automated coding accessibility and cloud-based applications are becoming
popular.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 11 | Nov 2023 www.irjet.net p-ISSN: 2395-0072

1.2 History of Quantum Computing
It is true behind the fact that Quantum Computers and computing were proposed by Richard Feynman and Yuri Manin in
the 1980s. The intuition behind Quantum computing stemmed from remarkable scientific progress faced with the inability to
model even systems in Physics [2].
1.2.1 Hybrid Quantum Computing:
It is the processes and architecture of a classical computer and a quantum computer working together to solve a problem.
The main idea behind this is that classical computers are used in Quantum Computing to define quantum gates, control the
configuration of the quantum computer, handle jobs, and process results from the quantum computer [1]. Due to the
advancement in quantum technology, the integration of classical and quantum computers is increasing. Here comes, Microsoft
developed a precise taxonomy like Batch Quantum Computing, Interactive quantum computing, integrated quantum
computing, and Distributed quantum computing.
2. Machine Learning
Machine learning is a branch of artificial intelligence and computer science that is mostly focused on the use of algorithms
and data to resemble the way that humans learn, which slowly improves its accuracy. Machine Learning is a subset of AI, and
the study of making machines to be more human-like in their behavior and decisions by giving them the ability to learn and
develop their programs, which is done with minimum human intervention as in no explicit programming.
Machine Learning has its place in computing because it is used widely in many industries such as finance, e-commerce, and
healthcare, and you are open to a wide range of career opportunities. They also can be used to build intelligent systems to
make predictions and decisions based on the data given. They are an essential tool for data analysis and visualization. Machine
Learning allows us to get insights and patterns from large datasets used to understand complex systems [3]. It is a rapidly
growing field with many research opportunities and developments.

There are 7 steps in Machine Learning which are gathering data, preparing the gathered data, choosing a model, Training,
Getting evaluated, hyperparameter tuning, and Prediction. Machine Learning is important because it gives a view of trends in
customer behavior and business patterns. Machine learning is a central part of Facebook, Google, and Uber’s operations.
There are 4 distinct types of Machine Learning which are,
 Supervised learning
 Unsupervised learning
 Semi-supervised learning
 Reinforcement learning.
There are 4 main benefits of machine learning which are natural language processing which allows machine learning to
process language-based inputs from humans, recognizing images which recognizes images and separates them into different
categories, Data mining which accesses data, finds patterns, and identifies spam emails, assesses credit risks, and detect fraud
attempts.
The advantages of Machine Learning are it easily identifies trends and patterns, doesn’t need a single bit of human
intervention, and has wide applications, on the other side there are some disadvantages, which are time and resources, it
needs a lot of time to process and a massive number of resources to process. The second disadvantage is Data, it requires a
large amount of data that needs to be inclusive, unbiased, and of good quality. The last disadvantage is the High error
susceptibility, which means Mistakes can have a chain of errors that can go unsolved for a long time. When they are going to
get solved, it takes a long time to recognize the issue, and a longer time to correct it.
3. Quantum Computing
Quantum computing, a rapidly emerging technology that harnesses the law of quantum mechanics to solve complex problems
that normal computers can’t solve. Normal computers do everyday things like representing data, processing it, and controlling
mechanisms. But, Quantum Computing is a multidisciplinary field that includes computer science, physics, and mathematics
and brings the services of quantum mechanics to solve complex problems much faster compared to classical computers. Both
research in hardware and application development paves the road for quantum computing.
3.1.1 Concepts in Quantum Computing
It is a fact that classical computers operate on bits, the same with quantum computers also. The main difference is with the
representation of quantum bits by 0 or 1 or a combination of both. A few concepts in Quantum computing are [4]:
 Qubit: It is a bit which represents the value of 0 or 1 or combination of both. It is also called quantum bit.
 Superposition: the linear combination of quantum bits 0 or 1 is called Superposition. Group of qubits in superposition
creates complex and multidimensional computational spaces which provides the opportunity to represent complex
problems in different new ways.
 Entanglement: It is an effect that correlates the behavior of two separate things. As per the research, when two qubits
are entangled, changes to one qubit directly impact the other.
 Interference: The environment of entangled qubits placed into a state of superposition creates waves of probabilities,
which are the outcomes of a measurement of the system. So, it brings in two forms of interference, these waves can
build on each other when many of them peak at a particular outcome or cancel each other out when peaks and
troughs interact.
3.1.2 Benefits of Quantum Computing
The pros of Quantum computing are:
 It excels at complex problem solving so problems can get their solutions faster, and the properties of qubits
let them solve problems quicker and more efficiently than traditional computers.
 Quantum technology is advantageous for science, pharmaceutical research, subatomic physics, and logistics.

 Quantum error correction and post-quantum cryptography, and Unintended consequences by Quantum-
powered AI.
Quantum Computing has a wide option of uses. The seven main uses of Quantum Computing are.
 AI and Machine Learning can be used to discover ways to automate and optimize tasks.
 Financial modeling can be used to better model the investments and securities at scale.
 Cybersecurity in which quantum computing has data encrypted while it is in use, then also provides both in-
transit and at-rest protections.
3.1.3 Application Areas:
Because of the high cost, most of the businesses have not used their application in quantum computing. Few businesses have
started or started to think of quantum computing in their business:
 JP Morgan chase
 IBM - ???/
 Microsoft – Microsoft Axure Quantum Platform
 D-Wave
 Rigetti Computing
3.1.4 Limitations
 But there is a big problem with quantum computing, which is qubit decoherence. This is a big problem because qubits
are sensitive to the environment so small disturbances can make them lose their quantum properties which are called
decoherence.
 Route and traffic development processes the data in real-time and adjusts routes for an entire group of vehicles.
 Manufacturing that runs more accurate and realistic prototyping also reduces the cost of prototyping, and outputs
better designs that don't necessarily need testing.
 Drug and Chemical research, in which the development of chemical compound and drugs, also research on its
interactions with other elements.
 Batteries help manufacturers incorporate new materials into batteries and semiconductors and help understand
lithium compounds and batteries.
 Spotting patterns in Large Datasets
 Facilitating Integration of Diverse Data Sets
 Optimizing Solutions

There are so many pros and cons but there is a significant controversy in quantum computing. As said before, Quantum
computing finds solutions to complex problems, but the machine’s ability to make solutions with flaws in its code can result in
unintended and unanticipated outcomes. This relates to the qubit decoherence as the errors in code can decrease the quality
of computation.
It experiences a few challenges such as,
 Error Correction
 Scalability
 Hardware and Software Development
 Classical Computers Interfaces
 Standards and Protocols
 Trained Talent
 Overall higher expenses.
4. Quantum Machine Learning
Quantum Machine Learning details the research that combines the integration of quantum algorithms with machine
learning concepts and applications. It also extends the pool of hardware for machine learning concepts and algorithms by a
computing device, known as a quantum computer. It resides with the law of physics, and quantum theory to handle the
information processing. Quantum Machine Learning is mainly built on two concepts, quantum data and quantum classical
models. Quantum Data are natural data that occur in a quantum system. Some examples of Quantum Data are, Quantum
metrology, Quantum communication networks, and chemical simulation. There is a possibility that quantum data generated
are noisy and so it is entangled before handling any measurement [5]. Then, Machine learning algorithms are used to create
models to handle the objective of the problems, like Classification, segmentation, etc. The Quantum Computing with Machine
Learning combinations are discussed in four options [5] as,
 Classical-Classical approach: The quantum-inspired algorithms are applied on classical data which could run on
classical computers.
 Classical-Quantum approach: The quantum machine learning algorithms are applied on classical data to
increase the efficiency of machine learning tasks.
 Quantum-Classical approach: Classical Machine Learning algorithms are applied to facilitate quantum
computers to get valuable insights of quantum data.
 Quantum-Quantum approach: Both quantum data and algorithm are great combinations to manipulate the
quantum states.
The translation of the Support Vector Machine and Classical K-means algorithm as quantum support vector machine and
quantum k-means algorithm are considered as suitable examples of quantum machine learning.
4.1 Impact of Quantum Machine Learning on real-world problems
Quantum Machine Learning (QML) has more advantages or benefits over machine learning algorithms. The research
of QML from different authors helps to solve NP & P problems and is shown in the table below.
Problem identified Proposed Limitations
Image Processing Quantum realization of the
nearest neighbor value
interpolation method for INEQR
[9].
The Quantum circuit used can
be optimized in terms of
complexity and the model can
be generalized for all-scale
rations.
3.1.5 Challenges
The wider growth in technology introduces Quantum Computing with benefits, but still,

Pattern Classification  Quantum Computing
for Pattern
Classification [7].
 Quantum decision tree
classifier [8].
 The algorithm has
limitations for
continuous inputs.
 Doesn’t suit for missing
attribute values and
training data with
quantum noise.
Pattern recognition Quantum Pattern Recognition –
Quantum Associative memory
based on pattern recognition is
proposed [6].
Identification efficiency could
not be tuned.
Natural Language Processing QNLP in Practice: Running
Compositional Models of
Meaning on a Quantum
Computer [10].
Scalability issues as the size of
vocabulary increases.
Also, it justifies that QML is applied to different domains like communication networks, privacy preservation, bioinformatics,
image processing, video processing, NLP and so many. The growth in technology and the tremendous amount of data and its
processing make it very hard to find solutions for real-world problems.
In relation to its impact on Artificial Intelligence (AI), Quantum Computing boosts the potential of AI by amplifying its velocity,
efficacy, and precision. This remarkable advancement empowers quantum computing to find its applications across diverse AI
use cases like Medical Care, Financial applications, maritime logistics, semiconductors, electric vehicles, etc. Quantum
computing enables faster learning and robust simulations for real-world problems by ensuring lifelong learning without losing
previous knowledge.
Conclusion
Computing is the basic version of Quantum Computing. Quantum computing can use qubits, which can be 1 or 0 at the same
time but computing uses bits, which can only be 1 or 0. Quantum Computing’s power grows faster in relation to the number of
qubits linked together, and it is 158 million times faster than a normal computer. Quantum Computing is filled with complex
conditions that help to solve complex problems and find a solution faster and more effectively. Quantum computing is going to
change the world with new materials and innovative solutions to old problems and improvised solutions to old problems with
answers that might be devised to fit the present world.
Reference:
1. https://cs.calvin.edu/activities/books/processing/text/01computing.pdf
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3. https://www.ncbi.nlm.nih.gov/books/NBK538701/
4. https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-quantum-computing.
5. https://www.tensorflow.org/quantum/concepts
6. Trugenberger, Carlo A. (2002) “Quantum pattern recognition” Quantum Information Processing 1(6) :471–493.

7. Schuld, Maria, Ilya Sinayskiy, and Francesco Petruccione. (2014) “Quantum computing for pattern classification” in Pacific
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