Very basic introduction to quantum computing given at Indaba Malawi 2022. Overviews some basic hardware in classical and quantum computing, as well as a few quantum machine learning algorithms in use today. Resources for self-study provided.

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3. Machine Learning
 Algorithms trained on data that are
useful in solving lots of problems in
industry:
 Translating speech to text in
another language
 Prediction of customer churn
 Segmentation of customer bases
 Forecasting crop yield
 Predicting professional contacts
not in one’s network

4. Classical Computer
Circuit Design
 Binary bits
 Electric circuits (wires with logic
gates)

5. Translation of
problem to (matrix-
based) algorithm
Algorithm code
compiled to
machine code
Compiled code
runs on bit-based
circuits

6. Quantum
Computers:
Leveraging
Physics

7. Particle-based qubit with physical
circuit
Superposition of binary bit states
Rotation gates inducing non-
binary values
Many more types of circuits to
manipulate qubit

8. Mapping (usually
via QUBO)
Algorithm focuses
on physical
operations
Hardware
executes
algorithm

9. • Faster solutions to combinatorics
problems (including many graph
algorithms)
• Possible solutions where classical
computing takes too long to ever
compute
Massively
parallel
optimization
through
superposed
states
• Usually by finding good quantum
operators and circuit design
• Improves performance and speed
of deep learning algorithms
Learning
efficiency

10. Quantum
neural
networks
Quantum
annealing
Quantum
graph
algorithms
Hybrid
algorithms
(quantum
genetic
algorithms)

11.  https://blog.paperspace.com/beginners-guide-to-quantum-
machine-learning/
 https://www.amazon.com/Quantum-Machine-Learning-
Computing-Mining/dp/0128100400
 https://pennylane.ai/blog/2021/10/how-to-start-learning-
quantum-machine-learning/