Noisy intermediate scale quantum computers are useful for various tasks including quantum state preparation, quantum metrology and variational quantum algorithms. However, the non-euclidean quantum geometry of parameterized quantum circuits is detrimental for these applications. Here, we introduce the natural parameterized quantum circuit (NPQC) with a euclidean quantum geometry. The initial training of variational quantum algorithms is substantially sped up as the gradient is equivalent to the quantum natural gradient. NPQCs can also be used as highly accurate multi-parameter quantum sensors. For a general class of quantum circuits, the NPQC has the minimal quantum Cramér-Rao bound. We provide an efficient sensing protocol that only requires sampling in the computational basis. Finally, we show how to generate tailored superposition states without training. These applications can be realized for any number of qubits with currently available quantum processors.