Quantum optimal control with quantum computers: A hybrid algorithm featuring machine learning optimization
Year: 2021
Type: article
Abstract: 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.
Source: Physical Review A
Authors: Davide Castaldo, Marta Rosa, Stefano Corni
Institution: University of Padua
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: European Research Council
Award: G4433873159
OpenAlex ID: W3040573296