Toca Siguiente (o usa las teclas de flecha) para avanzar idea por idea. Al final te espera una verificación fija de tres preguntas: el punto de control del curso, con las mismas preguntas en cada intento. La ← en la parte superior te permite salir cuando quieras; el progreso se guarda.
The variational loop
Choose a parameterized circuit ansatz. Run the circuit and measure an objective. Use a classical optimizer to update parameters. Repeat until convergence or budget exhaustion.
Variational methods trade deep circuits for repeated noisy evaluations.
VQE and QAOA
VQE estimates molecular or material energies by minimizing an expectation value. QAOA targets combinatorial optimization using alternating problem and mixing operators. Both are families of methods rather than guaranteed advantage machines.
A variational label does not establish usefulness; ansatz quality, measurement cost, and classical optimization matter.
Barren plateaus and measurement cost
Gradients can vanish as circuits scale, optimization landscapes can be difficult, and estimating many observables can require enormous numbers of shots. Problem-informed circuits and measurement grouping may help.
Near-term feasibility is often limited by sampling and optimization, not only qubit count.
Applied activity
Complete a simulator or analysis exercise: reproduce the lesson's central example, record assumptions and outputs, and explain one source of error or limitation.
Leer el texto completo de la lección
1. The variational loop
Choose a parameterized circuit ansatz. Run the circuit and measure an objective. Use a classical optimizer to update parameters. Repeat until convergence or budget exhaustion.
Variational methods trade deep circuits for repeated noisy evaluations.
2. VQE and QAOA
VQE estimates molecular or material energies by minimizing an expectation value. QAOA targets combinatorial optimization using alternating problem and mixing operators. Both are families of methods rather than guaranteed advantage machines.
A variational label does not establish usefulness; ansatz quality, measurement cost, and classical optimization matter.
3. Barren plateaus and measurement cost
Gradients can vanish as circuits scale, optimization landscapes can be difficult, and estimating many observables can require enormous numbers of shots. Problem-informed circuits and measurement grouping may help.
Near-term feasibility is often limited by sampling and optimization, not only qubit count.
4. Applied activity
Complete a simulator or analysis exercise: reproduce the lesson's central example, record assumptions and outputs, and explain one source of error or limitation.
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