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QI: Fachverband Quanteninformation

QI 12: Poster I

QI 12.7: Poster

Tuesday, March 19, 2024, 11:00–14:30, Poster B

Quantum processors for reinforcement learning — •edison argüello and sabine wölk — DLR (Deutsches Zentrum für Luft- und Raumfahrt)

Many quantum algorithms, such as e.g. the implementation of the hybrid learning agent described by Hamann and Wölk,^[1] require conditional multiqubit gates such as the Toffoli gate. A standard implementation of the Toffoli gate can be constructed from single qubit T- and Hadamard-gates and a minimum of 6 CNOT gates. However, in present quantum computer hardware, CNOT-gates are one of the main sources of errors prohibiting conditional quantum gates with a large number of control-qubits. In this poster, we present our investigation about a more direct implementation of a Toffoli gate in a fully coupled 3-qubit system with a Ising-like interaction. We expect that this new approach will reduce the Toffoli gate time and thus the resulting error compared to an implementation based on CNOT-gates.

References

1. Hamann A., Wölk S. (2022). Performance analysis of a hybrid agent for quantum-accessible reinforcement learning. New Journal of Physics 24(3), 033044.

Keywords: toffoli gate; spin ions; quantum algorithm