Overview of quantum computing and it's application in artificial intelligence

Guide: Presented by:
Mrs.Marina Abhishek P Rajesh
DEPT OF CSE S7 CSE
College Of Engineering Batch No:1
Kottarakkara CEK19CS001
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Contents
1. Abstract
2. Introduction
3. The Basic Building Blocks of Quantam Computing
3.1 A Quantam Computer
3.2 Qubits
3.3 Superpositions
3.4 Entaglement
3.5 Qubit Gates
4. Measurements
5. Clustering Problem in Quantam Computing
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Contents(Contd...)
6. Quantam Computing Applications in AI
7. Quantam Computing Algorithms in AI
7.2 Quantam Principal Component Analysis
7.3 Quantam Support Vector Machine
7. Pros and Cons
8. Conclusions
9. References
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1.Abstract
This Paper presents the basic building blocks of Quantam Computing,
how it's different from Classical Computing and how it can be
useful in the solving many real computing problems efficiently than
a classical computer. It also review's the main applications in Artificial
Intelligence that can be addressed more efficiently using the quantam
computers of today.
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2.Introduction
• Quantam Computing is the computer technology based on the
principles of quantam theory.
• Quantam Theory explains the nature and behaviour of energy and
matter on quantam(atomic and Subatomic level).
• Quantam Computing is essentially harnessing and exploiting the
amazing laws of quantam mechanics to process information.
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3.The Basic Building Blocks Of
Quantam Computing
3.1 A Quantam Computer
IBM's Quantam Computer
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3.2 Qubits
• A Classical Computer has a memory made of bits. A quantam
computer maintains a sequence of qubits.
• A Qubit is the most basic unit of operation in quantam computing
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Qubits(Contd..)
• The Physical representation of could use the two energy levels of an
atom. An excited state representing |1> and a ground state
representing |0>
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Qubits(Contd..)
• This Sphere is often called the Bloch Sphere, and it provides a useful
means to visualize the state of a single qubit
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3.3 Superposition
• A qubit is not always either in |0> or |1> , a quantam bit can also
represent both a linear combination of 1 and 0 at the same time,
this is known a Superposition.
• A qubit in Superposition is both |0> and |1> at the same time.
• Mathematically can be represented by an arbitrary state |ψ>,
|ψ> = α|0> + β|1>
where α and β are complex numbers
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Superposition(Contd..)
• A Quantam computer with n qubits can be in a arbitrary
Superposition of up to 2n different states simultaneously.
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3.4 Entaglement
• Entaglement is the ability of quantam system to exhibit correlations
between states within a superposition.
• Unlike Ordinary bits, qubits can be linked in a way that has no
analog in the digital world, this linkage acts instantaneously
over any distance.
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3.5 Qubit Gates
• In Digital Computing, Computations are done through logic gates.
In the same way, A qubit "gate" perform operations on qubits .Each
gate can be considered as matrix multiplication filter.
a) Hadamard Gate(H):
This gate is used to transform the qubit to a superposition
State.
For example applying H to |0> state ,H|0> =
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Qubit Gates(Contd..)
b)Pauli-X Gate:
c)Pauli-Y Gate:
d)Pauli-Z Gate:
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4.Measurements
• Measurements is converting quantam information into classical
bits.
• We can't measure the superposition state itself, instead we use special
quantam algorithms to do our calculations.
• A significant principle of Quantam Mechanics is that measurement
Outcomes are probabilistic.
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5. Clustering Problem in Quantam Computing
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6. Quantam Computing Applications in AI
• Quantam AI is the use of quantam computing for the computing of
machine learning algorithms.
• Quantam Computing can provide a computational boost to
AI, enabling it to tackle more complex problems
• It can handle large data sets faster and more efficiently than
Traditional AI technologies.
• It can achieve results that are not possible to achieve with classical
computers.
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7.Quantam Computing Algorithms in AI
7.1 Quantam Principal Component Analysis
• PCA is a dimensionality reduction algorithm used in
Machine Learning to reduce the number of features.
• It is an Unsupervised Machine Learning algorithm.
• The goal of PCA is to pick the k most important features that
captures the most variance in the data.
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7.2 Quantam Support Vector Machine
• SVM is Supervised Machine Learning Algorithm used for
Binary Classification.
• The goal of the SVM algorithm is to create the best line or decision
boundary that can segregate n-dimensional space into classes so that
we can easily put the new data point in the correct category in the
future.
• This best decision boundary is called a hyperplane.
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7.Pros and Cons
PROS: CONS:
1.Drug Discovery 1.Internet Security
2.Artificial Intelligence 2.The low temperature needed
3.Image and Speech Recognition 3.Algorithm creation
4.High Privacy 4.Difficult to build
5.High Processing Speed
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8.Conclusion
• Quantam Computing could provide a radical change in the way
computation is performed.
• Recently, IBM launched its most powerful Quantam Computer
With 433 qubits called Osprey.
• Future Computers – Hybrid of both types of computers
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9.References
[1] Nahed Abelgaber,Chris Nikolopoulus, Overview on Quantam Computing
and its Applications in Artificial Intelligence, 2020 IEEE Third International
Conference on Artificial Intelligence and Knowledge Engineering(AIKE)
[2] Barzen, J., Leymann, F. (2019). Quantum humanities: a vision for quantum
computing in digital humanities. SICS Software-Intensive Cyber-Physical
Systems
[3] Coles, P. J., Eidenbenz, S., Pakin, S., Adedoyin, A., Ambrosiano, J., Anisimov,
P., … Gunter, D.(2018). Quantam Algorithm implementations for beginners,
ArXiv preprint arXiv:1804.03719
[4] Rafael Sotelo, Quantam Computing:What,Why,Who. ,CHILECON 2019,
October 29-31, Valparaiso, Chile
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