Example Concept: Variational algorithms use parameterized quantum circuits where the parameters are adjusted through classical optimization to find the minimum energy state of a quantum system. The classical optimizer evaluates the cost function, typically the expectation value of the Hamiltonian, and updates the parameters to improve the circuit's performance despite the presence of noise. This iterative process allows the algorithm to converge towards an optimal solution while compensating for errors introduced by noisy qubits.