The paper introduces a classical-quantum hybrid algorithm called Quantum Circuit Learning (QCL) for machine learning tasks on near-term quantum processors. QCL leverages a low-depth quantum circuit to learn tasks by iteratively optimizing parameters, avoiding the need for high-depth circuits. The authors theoretically show that a quantum circuit can approximate nonlinear functions and confirm this through numerical simulations. QCL is compared with other quantum machine learning algorithms like Quantum Variational Eigensolver (QVE) and Quantum Approximate Optimization Algorithm (QAOA), highlighting its ability to handle complex functions and classification tasks. Numerical simulations demonstrate QCL's capability to fit nonlinear functions, classify data, and model dynamics of a 10-spin system with a 6-qubit circuit. The framework's potential quantum advantage lies in its ability to represent complex functions using exponentially many functions with respect to the number of qubits, which is intractable for classical computers. The unitarity condition of the quantum circuit helps avoid overfitting, further enhancing its performance.The paper introduces a classical-quantum hybrid algorithm called Quantum Circuit Learning (QCL) for machine learning tasks on near-term quantum processors. QCL leverages a low-depth quantum circuit to learn tasks by iteratively optimizing parameters, avoiding the need for high-depth circuits. The authors theoretically show that a quantum circuit can approximate nonlinear functions and confirm this through numerical simulations. QCL is compared with other quantum machine learning algorithms like Quantum Variational Eigensolver (QVE) and Quantum Approximate Optimization Algorithm (QAOA), highlighting its ability to handle complex functions and classification tasks. Numerical simulations demonstrate QCL's capability to fit nonlinear functions, classify data, and model dynamics of a 10-spin system with a 6-qubit circuit. The framework's potential quantum advantage lies in its ability to represent complex functions using exponentially many functions with respect to the number of qubits, which is intractable for classical computers. The unitarity condition of the quantum circuit helps avoid overfitting, further enhancing its performance.