Introduction to
Quantum Machine
Learning
Syed Farhan
Founder, QPower Research
Microsoft Student Ambassador
DISCUSSION
POINTS
Overview
Introduction to Quantum Systems
Qubits & Properties
Quantum Entanglement
Quantum Entanglement with Q#
ML Overview
QML Intuition
ML vs QML
Applications of QML
Demo of Classification using Q#
Summary
IBM promises 1000-qubit quantum computer—a
milestone—by 2023
Source: newsroom.ibm.com
QUANTUM IN THE
NEWS
Quantum Computer with Quantum Volume 64
Source: Honeywell.com
64 Quantum Volume Cloud Accessible Computer
Source: newsroom.ibm.com
IBM Q System
APPLICATIONS OF QUANTUM
COMPUTERS
Source: ibm.com
CLASSICAL COMPUTERS
Classical Computing
QUBITS
IBM Q System
Superposition
HADAMARD GATE
MEASUREMENT
QUANTUM ENTANGLEMENT
"Spooky Action at a
distance"
Albert Einstein
Bell Circuit With Microsoft Q#
Approaches to
Machine Learning
CC Overview
Hybrid Quantum-Classical: Steps for QML
1. Quantum
Embeddings
2. Quantum
Variational Circuit
3. Classical
Optimizer
Finance
Portfolio Analysis in Finance
Classification Problems
Possibility of classifying very large and
complex datasets, such as whether cells
are cancerous based on several factors,
at a higher speed and lower
computation cost.
Topological Analysis
Hybrid implementations of small-scale
quantum computing and powerful
classical computing for very large
datasets
WHERE CAN QML BE
USED?
SUMMARY
QML is all about uplifting the features of Quantum
Computing to do Machine Learning1.
It’s not about speedup, but looking at something
different entirely
2.
We can expect Hybrid Quantum-Classical
Architectures in the future
3.
Simple Classifier
with
Microsoft Q#
THANKS FOR
ATTENDING!
Follow us on LinkedIn for weekly dose of
Quantum Computing: bit.ly/qpowerlinkedin
Feel free to reach out for any questions
Email: farhan.tuba@gmail.com for
comments or questions.
You can sign up for Qpower’s
Quantum Program here:
bit.ly/qpower101

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