Isabell Heider & David Alber
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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The challenge of learning quantum computing
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Key players and platforms in quantum computing
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