This paper introduces a feedback-based quantum algorithm, the Counterdiabatic Feedback-Based Quantum Algorithm (CD-FQA), which enhances the Feedback-Based Quantum Algorithm (FQA) by integrating quantum Lyapunov control (QLC) with counterdiabatic driving. The CD-FQA introduces an additional control field inspired by counterdiabatic driving, enabling faster population transfer to low-energy states in quantum many-body systems. Simulations on one-dimensional Ising spin chains show that CD-FQA significantly reduces the time required to prepare ground states compared to conventional FQA, leading to a reduced quantum circuit depth. The algorithm is validated on IBM's cloud quantum computer, demonstrating its effectiveness in accelerating quantum computations for many-body systems and combinatorial optimization problems. The CD-FQA is based on QLC, which uses a Lyapunov function to guide a quantum system from an arbitrary initial state to a target state. The algorithm constructs quantum circuits iteratively, with parameters determined through feedback from qubit measurements. The CD-FQA includes an additional unitary inspired by counterdiabatic driving, which helps in reducing the circuit depth and improving the efficiency of state preparation. The algorithm is applied to various Ising models, showing improved performance in ground-state preparation. The CD-FQA's performance is evaluated for different parameters, including the longitudinal and transverse fields in Ising models. The results show that the choice of the counterdiabatic operator significantly affects the algorithm's performance. The CD-FQA with the Y operator performs best in most cases, while other operators like YZ and YX show varying degrees of success. The algorithm is also tested on cloud quantum computers, demonstrating its practical feasibility. The study highlights the importance of selecting appropriate control Hamiltonians in CD-FQA to achieve efficient ground-state preparation. The results show that the CD-FQA outperforms the standard FQA in terms of energy reduction and convergence speed, particularly for systems with a longitudinal term in the Hamiltonian. The algorithm's effectiveness is further demonstrated through simulations on quantum computers, showing its potential for practical quantum computing applications.This paper introduces a feedback-based quantum algorithm, the Counterdiabatic Feedback-Based Quantum Algorithm (CD-FQA), which enhances the Feedback-Based Quantum Algorithm (FQA) by integrating quantum Lyapunov control (QLC) with counterdiabatic driving. The CD-FQA introduces an additional control field inspired by counterdiabatic driving, enabling faster population transfer to low-energy states in quantum many-body systems. Simulations on one-dimensional Ising spin chains show that CD-FQA significantly reduces the time required to prepare ground states compared to conventional FQA, leading to a reduced quantum circuit depth. The algorithm is validated on IBM's cloud quantum computer, demonstrating its effectiveness in accelerating quantum computations for many-body systems and combinatorial optimization problems. The CD-FQA is based on QLC, which uses a Lyapunov function to guide a quantum system from an arbitrary initial state to a target state. The algorithm constructs quantum circuits iteratively, with parameters determined through feedback from qubit measurements. The CD-FQA includes an additional unitary inspired by counterdiabatic driving, which helps in reducing the circuit depth and improving the efficiency of state preparation. The algorithm is applied to various Ising models, showing improved performance in ground-state preparation. The CD-FQA's performance is evaluated for different parameters, including the longitudinal and transverse fields in Ising models. The results show that the choice of the counterdiabatic operator significantly affects the algorithm's performance. The CD-FQA with the Y operator performs best in most cases, while other operators like YZ and YX show varying degrees of success. The algorithm is also tested on cloud quantum computers, demonstrating its practical feasibility. The study highlights the importance of selecting appropriate control Hamiltonians in CD-FQA to achieve efficient ground-state preparation. The results show that the CD-FQA outperforms the standard FQA in terms of energy reduction and convergence speed, particularly for systems with a longitudinal term in the Hamiltonian. The algorithm's effectiveness is further demonstrated through simulations on quantum computers, showing its potential for practical quantum computing applications.