2020/07/31 by Davide Castaldo, Marta Rosa, Stefano Corni
Computer Science · Physics and Astronomy · #Algorithm #Artificial intelligence #Computer science #Evolutionary algorithm #Neural Networks and Reservoir Computing #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum algorithm #Quantum computer #Quantum mechanics #Quantum network #Quantum phase estimation algorithm #Wave function #physics.chem-ph #physics.comp-ph #quant-ph
paper · pdf · doi:10.1103/physreva.103.022613
published as Phys. Rev. A 103, 022613 (2021) · Final accepted version. Copyright APS
arxiv created 2021/02/24 · openalex publication_date 2021/02/24 · arxiv updated 2021/02/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We develop a hybrid quantum-classical algorithm to solve an optimal population transfer problem for a molecule subject to a laser pulse. The evolution of the molecular wave function under the laser pulse is simulated on a quantum computer, while the optimal pulse is iteratively shaped via a machine learning (evolutionary) algorithm. A method to encode on the quantum computer the n-electrons wave function is discussed, the circuits accomplishing its quantum simulation are derived and the scalability in terms of number of operations is discussed. Performance on noisy intermediate-scale quantum devices (IBM Q X2) is provided to assess the current technological gap. Furthermore the hybrid algorithm is tested on a quantum emulator to compare performance of the evolutionary algorithm with standard ones. Our results show that such algorithms are able to outperform the optimization with a downhill simplex method and provide performance comparable to more advanced (but quantum-computer unfriendly) algorithms such as Rabitz's or gradient-based optimization.