The document outlines a presentation by Peter Wittek on pragmatic quantum machine learning. It discusses why quantum computing is needed, what a quantum computer is, and different paradigms like gate-model and quantum annealing. Applications of quantum machine learning discussed include optimization, sampling, and unsupervised learning. Both gate-model and annealing quantum computers are probabilistic and useful for problems that are hard to parallelize classically. The key takeaway is that quantum computers are available now and efficient for non-parallelizable problems like optimization and sampling.

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AI Summit
Pragmatic Quantum Machine Learning Today
Peter Wittek, University of Toronto

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Agenda
Why Quantum Computing?
What Is a Quantum Computer?
Quantum Optimization
Quantum-Enhanced Sampling
Applications of Quantum
Machine Learning

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Landscape of Commercial Quantum Computing
D-Wave raises
$204M
in total
Alibaba commits
$15B

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Why Quantum Machine Learning?

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What Is Quantum Computing?

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Why Do We Need 1000s of Qubits?
Classical computer: 64 bits
A quantum computation is global.
Great for problems that are hard
to parallelize!

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Quantum Computing Paradigms
Gate-Model Quantum Computer Quantum Annealing

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Implementations
Superconducting Photonic Trapped Ions

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Hybrid Classical-Quantum Algorithms

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Quantum Annealing

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Optimization on Gate-Model Quantum Computers

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Use Case: Unsupervised Learning
Map an unsupervised problem
to a graph.
E.g., dense subgraph
identification

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Quantum Computers Are Probabilistic
Boltzmann distribution

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Probabilistic Graphical Models

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Markov Logic Networks with Quantum Sampling

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Quantum Software Industry

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Key Takeaway
Quantum computers are here!
Probabilistic and efficient for
non-parallelizable problems
Optimization and sampling
Don’t be left behind
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23. Thank you!
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Peter Wittek
https://peterwittek.com/