How quantum computing is used for data science applications and industrial uses related to data processing and pre-processing

1. Introduction to Quantum
Data Science
How Quantum Computing Is usable in Data Science

2. What is Quantum
Computing?
• Quantum computing is a cutting-edge field
that utilizes the principles of quantum
mechanics to perform complex
computations.
• Unlike classical computers that use bits,
quantum computers use quantum bits or
qubits, which can exist in multiple states
simultaneously.
• Quantum computers can solve certain
problems much faster than classical
computers.

3. How is Quantum
Computing Done?
In a classical computer, information is stored in bits, which
can be either 0 or 1. In a quantum computer, information is
stored in qubits, which can be both 0 and 1 at the same
time which is a property of a area of quantum computing
known as “Quantum Mechanics”.
This property of quantum mechanics, known as
superposition, allows quantum computers to perform
calculations that are impossible for classical computers.

4. Applications of Quantum Computing in Data Science

5. Uses of Quantum Computing in Data Science
Quantum computing has the potential to revolutionize data science and solve complex
problems in various domains from IT and Manufacturing to Agriculture and Supply chain.
Building Powerful Machine Learning models with Qauntum based processing power to
predict volatile events that otherwise cant be computed with no Accuracy.
Simulation of Experiments and events using raw ideas and variables to estimate risks.
Building Powerful cyber security applications with minimized hacking.

6. Quantum Machine Learning
• Quantum machine learning combines the
power of quantum computing with
traditional machine learning techniques.
• It explores the use of quantum algorithms
and quantum data structures to enhance
the performance of machine learning tasks
such as classification, clustering, and
regression.
• Quantum computers could be used to
analyze large amounts of data to identify
patterns that might indicate malicious
activity.
• Quantum machine learning holds great
promise in tackling complex data science
problems that are beyond the capabilities of
classical approaches.

7. Experimental Simulations Using Quantum Computing
• They can be used to study the behavior
of complex systems that are impossible
to simulate on classical computers.
• They can be used to design new
materials and drugs with improved
properties.
• They can be used to test new theories
and hypotheses.
• They can be used to improve our
understanding of the microscopic
scientific workings of the universe.

8. Cyber Security Applications with Quantum computing
• Quantum-resistant cryptography: Quantum
computers could be used to break current
encryption standards, which are based on
classical computing.
• Quantum-powered network
security: Quantum computers could be used
to design new network security protocols
that are more secure against quantum
based cyber attacks.
• Quantum-powered blockchain: Quantum
computers could be used to create more
secure blockchains, which are a type of
distributed ledger technology that is used to
record transactions. This could help to
prevent fraud and other malicious activity
on blockchain-based systems.

9. Industrial Applications of
Quantum Computing with
Data Science

10. Agriculture Growth with
Quantum Computing &
Data Analytics
• Developing new fertilizers: Quantum computing
could be used to design new fertilizers that are
more efficient and effective. This could help to
reduce the environmental impact of agriculture
and improve crop yields.
• Managing livestock: Quantum computing could be
used to develop new models of animal behavior
and help farmers to manage their livestock more
effectively. This could lead to improved animal
welfare and increased production.
• Optimizing water use: Quantum computing could
be used to model the water cycle and help farmers
to optimize their irrigation practices. This could
help to conserve water and improve crop yields.

11. Manufacturing Optimization with
Quantum Computing based data
analytics
• Optimizing production schedules: Quantum computing could be used to
optimize production schedules by examining a wide range of factors,
such as the availability of resources, the complexity of the products
being manufactured, and the demand for those products.
• Designing new products: Quantum computing could be used to design
new products by simulating the behavior of materials and components
at the atomic level. This could lead to the development of new products
that are more efficient, durable, and lightweight.
• Improving quality control: Quantum computing could be used to
improve quality control by identifying defects in products at an early
stage. This could lead to fewer defects and improved product quality.

12. Stock trading effectiveness
with Quantum based
predictive modeling
• Faster and more accurate predictions: Quantum
computers can process and analyze vast amounts
of data much faster than traditional computers.
This could allow us to make more informed
trading decisions and potentially achieve better
returns.
• New trading strategies: Quantum computing
could enable us to develop new trading strategies
that are not possible with traditional methods.
• Reduced risk: Quantum computing could help us
to reduce the risk of loss by making more
informed trading decisions.

13. Supply chain efficiency using
Quantum computing
applications.
• Routing and scheduling: Quantum computers can
be used to solve complex routing and scheduling
problems that are currently too difficult for
classical computers. This could lead to more
efficient delivery routes, better fleet
management, and reduced transportation costs.
• Inventory management: Quantum computers can
be used to optimize inventory levels and reduce
waste. This could be done by simulating different
scenarios and identifying the optimal inventory
levels for different products and locations.

14. Example use cases of Agriculture
Growth with Quantum Computing
& Data Analytics
Accenture is using quantum computing to
develop a smart growth calculator that can help
farmers to optimize the growth of their crops.
D-Wave Systems is working with the University
of California, Davis to develop quantum-
powered tools for crop breeding.
IBM is working with the University of Cambridge
to develop quantum-powered tools for
predicting crop yields.

15. Examples of Manufacturing
Optimization with Quantum
Computing based data analytics
Samsung collaborated
with Professor Myungshik
Kim, chair of Theoretical
Quantum Information
Sciences at Imperial
College London, and his
team to explore early-
stage quantum
algorithms. The research
team created a simulation
of the dynamics of an
interacting spin model, a
mathematical model used
to examine magnetism.
https://www.honeywell.c
om/us/en/news/2021/02
/samsung-explores-
quantum-computing-
possibilities
IBM is working with Ford
to develop quantum-
powered tools for
designing new products.
https://www.ibm.com/bl
og/ford-it-innovation-
award-ibm/
D-Wave Systems is
working with Volkswagen
to develop quantum-
powered tools for
optimizing production
schedules.
https://www.dwavesys.co
m/media/2pojgtcx/dwave
_vw_case_story_v2f.pdf

16. Examples of Stock trading effectiveness
with Quantum based predictive modeling
• No real world examples have been used yet due to
resource limitations for the use of quantum computing.
• A study, by researchers at the University of Waterloo,
showed that quantum algorithms could be used to develop
new trading strategies that are not possible with
traditional methods. The study used a quantum algorithm
called the quantum annealing algorithm to optimize
trading strategies. The results showed that the quantum
algorithm was able to develop trading strategies that
outperformed traditional strategies by an average of 20%.
• https://arxiv.org/pdf/1508.06182.pdf

17. Examples of Supply chain
efficiency using Quantum
computing applications.
• DHL: is researching on using quantum computing to
optimize its global freight network. The company has
plans to design a quantum algorithm to find the
shortest and most efficient routes for its shipments.
https://lot.dhl.com/quantum-computing-could-
transform-the-logistics-industry-within-the-next-
decade/
• Alibaba: aims to develop a quantum computer that
will be 100 times faster than classical computers
which will enable groundbreaking advances in Supply
chain route optimizations on a global scale in a few
minutes on a daily basis.
https://www.alibabacloud.com/blog/developments-
in-alibabas-cloud-computing-infrastructure_600073

18. Conclusion of Quantum Computing
with Data Science

19. How can small companies
implement quantum computing
and use it with data analytics
and machine learning
• Use quantum simulators: Quantum simulators are
software programs that can simulate the behavior
of quantum computers. This means that small
companies can experiment with quantum
computing without having to access a real
quantum computer.
• Partner with a quantum computing
provider: There are a number of companies that
offer quantum computing as a service. These
companies provide access to quantum computers
and software, so that small companies can
experiment with quantum computing without
having to invest in their own hardware.(Similar to
cloud services)

20. Can Quantum Computing be used in developing
economies for business applications? How so!
Partner with developed economies: Developing
economies can partner with developed economies
that have more experience with quantum
computing. This could help them access the
technology and expertise they need to implement
quantum computing.
Invest in education and training: Developing
economies can invest in education and training to
develop a skilled workforce that can work with
quantum computers. This would help them build
the capacity to adopt this technology.
Focus on specific applications: Developing
economies can focus on specific applications
where quantum computing can have a significant
impact. This could help them get the most out of
the technology and overcome the challenges of
adoption.

21. Examples of Developing Economies that
have Invested in Advanced Computing
Applications
• Pakistan built a Super computing lab in the 1970s, making it
one of the first countries in the world to do so. The lab was
located at the National Center for Physics (NCP) in Islamabad,
and it was headed by Dr. Raziuddin Siddiqui. The lab's research
focused on superconducting qubits. Superconducting qubits
are made up of tiny loops of superconducting material that
can be used to store quantum information.
https://www.ncp.edu.pk/ncp-history.php
• Thailand: There are Quantum Computing Initiatives being
launched in the private and public sector. The initiative aims to
develop quantum technologies and applications in areas such
as agriculture, manufacturing, and transportation.
https://qtft.org/about

22. Challenges of Quantum
Computing applications in
Industrial context
• Software development: There is a lack of mature software
development tools for quantum computing. This makes it
difficult to develop and deploy quantum applications.
• Error correction: Quantum computers are highly susceptible to
errors, which can significantly impact the accuracy of their
calculations. Error correction techniques are essential for
ensuring that quantum computers can be used for reliable
applications.
• Lack of qualified personnel: There is a shortage of qualified
personnel with the skills and knowledge necessary to develop
and deploy quantum applications.
• Legal and regulatory challenges: There are a number of legal
and regulatory challenges that need to be addressed before
quantum computing can be used in some industries. For
example, there are concerns about the potential for quantum
computers to be used for malicious purposes.

23. Tools used for building
course
• Microsoft Office.
• Chatgpt.
• Google Bard.
• Google Search engine.
• Google Scholar.

24. Researches on Quantum
Computing for Business context
• Quantum technology impact: the necessary workforce for developing quantum
software(2020)
https://ceur-ws.org/Vol-2561/paper1.pdf
• Quantum Computing and Deep Learning Methods for GDP Growth Forecasting
(2021)
https://link.springer.com/article/10.1007/s10614-021-10110-z
• Evolution of Quantum Computing: Theoretical and Innovation Management
Implications for Emerging Quantum Industry
https://ieeexplore.ieee.org/abstract/document/9800933
• Quantum Information Science: Applications, Global Research and
Development, and Policy Considerations (2019)
https://apps.dtic.mil/sti/pdfs/AD1169974.pdf
• Quantum technician skills and competencies for the emerging Quantum 2.0
industry (2022)
https://www.spiedigitallibrary.org/journals/optical-engineering/volume-61/issue-
8/081803/Quantum-technician-skills-and-competencies-for-the-emerging-Quantum-
20/10.1117/1.OE.61.8.081803.full?SSO=1