We propose a deep reinforcement learning (DRL) method, which doesn't require any underlying gate model or qubit pre-calibration, capable of controlling a ...

We employed the IBMQ platform and the. Qiskit Pulse toolkit to address such a specific quantum control task using the IBM Armonk transmon superconducting qubit.

This poster addresses a quantum control problem consistingof developing an alternative method to build a quantum logicgate by brief pulse sequences.

Duration: 4:13
Posted: Nov 28, 2022

We introduce a method based on differentiable programming to leverage explicit knowledge of the differential equations governing the dynamics of the system. In ...

Deep Reinforcement Learning Quantum Control on IBMQ Platforms and Qiskit Pulse. https://doi.org/10.1109/qce53715.2022.00108. Journal: 2022 IEEE International ...

Deep reinforcement learning quantum control on ibmq platforms and qiskit pulse. R Semola, L Moro, D Bacciu, E Prati. 2022 IEEE International Conference on ...

In an example task of binary classification, VQP learning achieves up to 11% and 9% higher accuracy compared with VQC learning on the qiskit pulse simulators ( ...

This work finds that DRL leads to robust digital quantum control with operation time bounded by quantum speed limits dictated by STA, and demonstrates that ...

Missing: Reinforcement Learning