Dr. Nancy Agnes discusses quantum machine learning, emphasizing its potential to solve complex problems beyond classical computing capabilities. The presentation covers the theoretical aspects of quantum computing, its application in machine learning, and the mathematical concepts required for implementation. The conclusion highlights the promise of quantum machine learning for the future of artificial intelligence while noting the need for further understanding and development.

1. An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Phdassistance
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2. Today's Discussion
Introduction
Why quantum machine learning
How to perform quantum machine learning
Math behind quantum machine learning
Conclusion

3. Introduction
In order to solve issues that are beyond the capabilities of classical computers, quantum computing
makes use of features of quantum physics. Qubits are used in a quantum computer.
Qubits are similar to conventional computer bits in that they can be placed in superposition and
transfer entanglement with other qubits. How quantum computers might impact machine learning is
an important topic of interest.
This is where quantum machine learning enters the picture; it is a theoretical area that is only now
beginning to take shape. It lies at the nexus of machine learning and quantum computing (Biamonte et
al., 2017)

4. Why quantum machine learning
There are various reasons to use quantum computing in machine learning realm, few of the reasons
are listed below:
Quantum computing can be used in Deep learning and machine learning problems to
reduce the time taken train the deep neural network. Many authors are currently
performing these techniques to solve the training time issue when considering the size of
dataset and deep learning model architecture learnable parameters.
The quantum state of a qubit in a quantum computer is a vector in a 2a-dimensional
complex vector space. In this area, numerous matrix transformations take place.
contd...

5. The Fourier Transform, identifying eigenvectors and eigenvalues, and resolving linear
sets of equations over 2-dimensional vector spaces can all be solved by quantum
computers in polynomial-time.
As discussed before about the eigen values and vectors, The computation of
eigenvectors and eigenvalues is another problem with traditional computers. The set of
related eigenvectors and eigenvalues increases in proportion to the input's higher
dimensionality.
Quantum Random Access Memory (QRAM), which selects a data vector at random,
enables quantum computers to handle this problem quickly and efficiently. It employs
qubits to translate the vector into a quantum state. Logarithmic qubits are included in
the summarized vector that results from quantum principal component analysis. A
dense matrix is created by the specified random vector.

6. V
1
U( )
V
V
2
3
3
2
1
U
U
( )
( )
Evaluate Gradients &
Update Parameters
Evaluate
Cost
Function
Prepare Quantum
Dataset
Evaluate Quantum
Model
Sample
or
Average
Evaluate
Classical
model

7. How to perform quantum
machine learning
contd...
There are some concepts in math and linear algebra that are
important to understand quantum machine learning.
Knowing some basic Python is also very useful if you want to
program an algorithm using some of the most popular
frameworks available.
Machine learning and quantum computing are two major
topics which needs to be understood in order to perform
quantum machine learning and quantum computing
(Cerezo et al., 2022) .

8. The goal of machine learning is to utilise computers to find trends in data and extrapolate
those patterns and trends to data that has never been seen before. It entails creating a
computer algorithm that, without being explicitly designed, may increase its performance on
a job (learn).
Quantum machine learning has many ideas that are connected to optimization. Identifying
the sources that will result in the best feasible output for a particular problem is the goal of
optimization problems, which can be found in a wide range of academic disciplines.
Once a cost function has been established, an optimization approach must be chosen.
Generally speaking, you reduce your objective functions by taking a set of actions that
ultimately result in the lowest cost (Schuld et al., 2015) .

9. Quantum Computing Machine Learning

10. Math behind quantum
machine learning
contd...
The study of both quantum computing and (quantum) machine
learning and quantum deep learning requires knowledge of a
number of mathematic concepts. Those are -
Linear algebra
Vectors and Matrices
Calculus
Vectorization
Eigen values and vectors
Gradient descent
1.
2.
3.
4.
5.
6.

11. Artificial intelligence has a bright future thanks to the computer science idea of quantum
machine learning. However, excitement also carries the risk of disinformation, especially since
quantum machine learninghave not yet realised its full potential and still need to have its
individual intricacies well understood.
Neural networks have a long way to go before quantum computing is used in them. However,
in light of the possibility that AI will run out of computer power in the not-too-distant future,
quantum machine learning may be the idea that offers AI the long-term renewal it requires.
Finally to know more about the interlinking between quantum theory and machine learning,
Please get in touch with PhD assistance. Our qualified specialists constantly strive to provide
the best assistance to PhD academics. Therefore, you may always get in touch with us for the
prompt standard service without any hesitation.
Conclusion

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