The applicability of reinforcement learning for the automatic generation of state preparation circuits

Gabor, Thomas; Zorn, Maximilian und Linnhoff-Popien, Claudia (2022): The applicability of reinforcement learning for the automatic generation of state preparation circuits. GECCO '22, Genetic and Evolutionary Computation Conference, Boston Massachusetts, July 9 - 13, 2022. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, New York: Association for Computing Machinery. S. 2196-2204

Abstract

State preparation is currently the only means to provide input data for quantum algorithm, but finding the shortest possible sequence of gates to prepare a given state is not trivial. We approach this problem using reinforcement learning (RL), first on an agent that is trained to only prepare a single fixed quantum state. Despite the overhead of training a whole network to just produce one single data point, gradient-based backpropagation appears competitive to genetic algorithms in this scenario and single state preparation thus seems a worthwhile task. In a second case we then train a single network to prepare arbitrary quantum states to some degree of success, despite a complete lack of structure in the training data set. In both cases we find that training is severely improved by using QR decomposition to automatically map the agents' outputs to unitary operators to solve the problem of sparse rewards that usually makes this task challenging.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• Ort: New York
• Bemerkung: ISBN 978-1-4503-9268-6
• Sprache: Englisch