Quantum Optimization (with IBM Quantum)Julien Gacon, Dr. Daniel J. Egger, Dr. Stefan Woerner, Lucía Cuervo Valor

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Week 1

In the first week you will learn the basics of how to carry out optimization tasks on quantum computers. We will learn about optimization classes, and in particular, quadratic unconstrained binary optimization. Next, we learn how to encode such problems in Ising Hamiltonians and how to tackle them with quantum annealing and variational algorithms such as quantum approximate optimization.

Lecture 1: Optimization problem classes

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Module 1: Optimization problem classes

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Lecture 2: From QUBO to Ising Hamiltonian

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Module 2: From QUBO to Ising Hamiltonian

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Lecture 3: Variational Quantum Eigensolver

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Lecture 3: Bonus Episode - Maxcut with Qiskit

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Module 3: Variational Quantum Eigensolver

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Lecture 4: The Adiabatic Theorem and Adiabatic Annealing

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Module 4: The Adiabatic Theorem and Adiabatic Annealing

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Lecture 5: From Adiabatic Annealing to QAOA

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Module 5: From Adiabatic Annealing to QAOA

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Material Week 1

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Week 2

In the second week we will study improvements and extensions to the algorithms from week 1 and how they can be executed on superconducting qubits. You will also learn about a non-variational approach to optimization problems on a quantum computer. Finally, there are two interviews that wrap up the course, where we discuss possible applications, challenges and an outlook for quantum optimization.

Lecture 6: Extensions of QAOA - part 1

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Lecture 6: Extensions of QAOA - part 2

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Module 6: Extensions of QAOA

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Lecture 7: Executing QAOA on Superconducting Qubits

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Module 7: QAOA on Superconducting Qubits

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Lecture 8: Advanced Variational Algorithms

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Module 8: Advanced Variational Optimization

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Lecture 9: Optimization with Grover Search

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Lecture 9: Bonus Episode - Portfolio Optimization with Grover Adaptive Search

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Module 9: Optimization with Grover Search

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Lecture 10: Possible Applications

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Module 10: Possible Applications

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Lecture 11: Challenges and Outlook

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Module 11: Challenges & Outlook

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Material Week 2

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