Deep Quantum Control: End-to-end quantum control using deep learning algorithms

The expected speedup of quantum algorithms on near-term quantum processors as well as the resource requirements for achieving quantum fault-tolerance relies on the fidelity of gate operations on available qubits. Traditionally, quantum control and mitigation protocols have been obtained via optimizing control trajectories by assuming simple a priori error models that impact the qubits, or the amplitude, frequency, and phase of the driving control fields. Although these models have been instrumental in achieving individual 1,2-qubit gates with average fidelities of 99-99.9%, however various sources of noise get mixed up and accumulated in running quantum circuits with larger numbers of qubits. Here we propose an "end-to-end" framework, which instead starts from direct experimental observations to obtain optimal quantum control trajectories that are sufficiently resilient to all sources of errors. We achieve this by combining existing quantum control and characterization techniques with the representation and learning power of modern deep learning algorithms. We demonstrate our framework to achieve high fidelity 1,2-qubit gates that are resilient to various sources of noise.