This paper introduces a curriculum-based reinforcement learning quantum architecture search (CRLQAS) algorithm designed to address the challenges of deploying variational quantum algorithms (VQAs) in realistic noisy quantum environments. The key contributions of the work include: 1. **CRLQAS Algorithm**: - **3D Architecture Encoding**: A tensor-based binary encoding scheme that captures the depth and structure of quantum circuits. - **Episode Halting Scheme**: A mechanism to steer the agent towards finding shorter circuits. - **Simultaneous Perturbation Stochastic Approximation (SPSA)**: A novel variant of SPSA for faster convergence in the presence of noise. 2. **Optimized Simulator**: - An optimized simulator that employs the Pauli-transfer matrix formalism in the Pauli-Liouville basis to simulate noisy quantum circuits efficiently, achieving up to six times faster computation. 3. **Experiments**: - Numerical experiments on quantum chemistry tasks (H₂, LiH, H₂O) demonstrate that CRLQAS outperforms existing QAS algorithms across several metrics in both noiseless and noisy environments. - The algorithm achieves chemical accuracy for a class of chemical Hamiltonians with better accuracy in terms of gate and depth efficiency. 4. **Discussion**: - The paper discusses the impact of noise on the performance of QAS algorithms and highlights the importance of robust optimization strategies. - The CRLQAS method is shown to be effective in handling realistic noise levels and achieving ground-state energy within chemical accuracy. 5. **Conclusion**: - The CRLQAS algorithm is demonstrated to be a state-of-the-art approach for quantum architecture search, offering significant improvements in both performance and efficiency. The paper also includes a detailed analysis of the experimental setup, hyperparameters, and noise profiles used in the simulations, ensuring reproducibility.This paper introduces a curriculum-based reinforcement learning quantum architecture search (CRLQAS) algorithm designed to address the challenges of deploying variational quantum algorithms (VQAs) in realistic noisy quantum environments. The key contributions of the work include: 1. **CRLQAS Algorithm**: - **3D Architecture Encoding**: A tensor-based binary encoding scheme that captures the depth and structure of quantum circuits. - **Episode Halting Scheme**: A mechanism to steer the agent towards finding shorter circuits. - **Simultaneous Perturbation Stochastic Approximation (SPSA)**: A novel variant of SPSA for faster convergence in the presence of noise. 2. **Optimized Simulator**: - An optimized simulator that employs the Pauli-transfer matrix formalism in the Pauli-Liouville basis to simulate noisy quantum circuits efficiently, achieving up to six times faster computation. 3. **Experiments**: - Numerical experiments on quantum chemistry tasks (H₂, LiH, H₂O) demonstrate that CRLQAS outperforms existing QAS algorithms across several metrics in both noiseless and noisy environments. - The algorithm achieves chemical accuracy for a class of chemical Hamiltonians with better accuracy in terms of gate and depth efficiency. 4. **Discussion**: - The paper discusses the impact of noise on the performance of QAS algorithms and highlights the importance of robust optimization strategies. - The CRLQAS method is shown to be effective in handling realistic noise levels and achieving ground-state energy within chemical accuracy. 5. **Conclusion**: - The CRLQAS algorithm is demonstrated to be a state-of-the-art approach for quantum architecture search, offering significant improvements in both performance and efficiency. The paper also includes a detailed analysis of the experimental setup, hyperparameters, and noise profiles used in the simulations, ensuring reproducibility.