Variational Quantum Algorithms, Cerezo, M., Arrasmith, A., Coles, R., Cincio, P., Fouquier, K., and Patrick, S., 2021Nature Reviews Physics, Vol. 3DOI: 10.1038/s42254-021-00348-9 - A comprehensive review of Variational Quantum Algorithms, covering the fundamentals, various ansatz designs, and applications in quantum machine learning and chemistry.
Barren plateaus in quantum neural network training landscapes, McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R., and Neven, H., 2018Nature Communications, Vol. 9DOI: 10.1038/s41467-018-07090-4 - This foundational work identifies and characterizes 'barren plateaus,' a significant challenge in training deep parameterized quantum circuits, where gradients vanish exponentially with the number of qubits.
Hardware-efficient variational quantum eigensolver for small molecules and materials, Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Chow, J. M., and Gambetta, J. M., 2017Nature, Vol. 549DOI: 10.1038/nature23879 - This paper showcases early experimental implementations of the Variational Quantum Eigensolver (VQE), demonstrating both problem-inspired (e.g., UCCSD) and hardware-efficient ansatz designs for molecular simulations.