Quantum Artificial Intelligence

Vorlesung an der Universität des Saarlandes, Fachrichtung Informatik, LSF 139530

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Lecture

1

Welcome

  • Course organization
  • Introduction: Quantum Computing and AI in a Nutshell
  • Q/A

Part I: Quantum Computation

2

Basic Concepts (I)

  • Qubits, quantum registers, operations and gates, entanglement, measurement
  • No-cloning theorem
     

3

Basic Concepts (II)

  • Quantum teleportation
  • Quantum programming frameworks
     

4

Quantum Algorithms (I)

  • Quantum parallelism and Deutsch-Josza Algorithm
  • Implementation with IBM QISKIT
     

5

Quantum Algorithms (II)

  • Quantum search and Grover’s Algorithm
  • Implementation with IBM QISKIT
     

6

Quantum Algorithms and Complexity

  • Quantum prime factorization and Shor’s Algorithm (overview)
  • Quantum complexity analysis
  • Adiabatic quantum computation in brief
     

Part II: Quantum Computation for AI

7

Quantum Machine Learning (I)

  • QML intro, fault-tolerant QML with quantum PCA and quantum SVM
  • Hybrid quantum-classical computing: Parameterized quantum gates, variational quantum algorithm for NISQ devices
     

8

Quantum Machine Learning (II)

  • Quantum support vector machines (hybrid approach) 
  • Implementation with IBM QISKIT
     

9

Quantum Machine Learning (III)

  • Quantum neural networks
  • Implementation with IBM QISKIT
  • Quantum reinforcement learning in brief
     

10

Quantum Optimization (I)

  • Quantum Approximate Optimization Algorithm (QAOA)
     

11

Quantum Optimization (II)

  • Adiabatic: Quantum annealing for NP-hard optimization problems
  • From NP-hard AI problem to gate-based or adiabatic quantum solution
  • Quantum Coalition Structure Generation
     

12

Selected Quantum AI Algorithms for Applications (I)

  • Gabriele Compostella (Quantum Team at Volkswagen AG):
    Quantum Computing for the Automotive Industry.
  • Quantum Computing Landscape

13

Selected Quantum AI Algorithms for Applications (II)

  • Quantum Flexible Job Shop Scheduling a.o.

Selected background references:

Quantum Computing

  1. Nielsen, M. & Chuang I. (2002): Quantum Computation and Quantum Information.
  2. Biamonte, J. et al. (2017): Quantum Machine Learning. Nature, 549(7671):195-202.
  3. Rieffel, E. G., & Polak, W. H. (2011). Quantum computing: A gentle introduction. MIT Press.
  4. Kaye, P., Laflamme, R., & Mosca, M. (2007). An introduction to quantum computing. Rinton Press.
  5. Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.

Artificial Intelligence

  1. Russell, S. & Norvig, P. (2021). Artificial Intelligence: A modern approach. 4th Ed., Pearson Education
  2. Mitchell, T. (1997): Machine Learning. McGraw-Hill
  3. Goodfellow, I.J. et al. (2016): Deep Learning. MIT Press,
  4. Nielsen, M. (2017): Neural Networks and Deep Learning.
  5. http://deeplearning.net
     
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