Isabell Heider & David Alber

Quantum computing for developers: Solving optimization problems with Qiskit

A quantum circuit makes a guess, and a classical computer refines it. See how this hybrid loop lets developers solve optimization problems on real quantum hardware today.

Quantum computing for developers: Solving optimization problems with Qiskit
#1about 5 minutes

Understanding the fundamentals of quantum computing

Quantum computers use qubits and quantum gates to solve problems that are intractable for classical computers, with hardware rapidly scaling.

#2about 4 minutes

Solving optimization problems with the VQE algorithm

The Variational Quantum Eigensolver (VQE) is a hybrid algorithm that uses both quantum and classical computers to find the minimum eigenvalue of an observable.

#3about 6 minutes

Building a VQE routine for a toy problem with Qiskit

A step-by-step guide to implementing a VQE routine in Python using Qiskit, including defining the observable, ansatz, objective function, and optimizer.

#4about 7 minutes

Executing VQE on a simulator and real quantum hardware

The VQE routine is first executed on a quantum simulator for prototyping and then run on a real IBM quantum computer by simply changing the backend.

#5about 5 minutes

Finding molecular ground state energy with error mitigation

A more advanced VQE application calculates the ground state energy of a hydrogen molecule, demonstrating how error mitigation techniques improve accuracy on real quantum hardware.

#6about 2 minutes

Summary and resources for getting started with quantum

Key takeaways emphasize the broad applicability of quantum computing and the value of learning now, with resources like Qiskit textbooks and IBM Quantum available for exploration.

#7about 5 minutes

Q&A on VQE advantages and practical considerations

The discussion addresses the advantages of VQE over classical methods, the heuristic nature of the algorithm, and the flexibility in designing the ansatz.

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