Quantum gate design with machine learning

Designing of fast and high fidelity quantum gates is crucial for getting the most out of current quantum hardware since detrimental effects of decoherence can in this way be minimised during the operation of the gates. However, achieving fast gates with high-fidelity and desirable efficiency on the state-of-the-art physical hardware platforms remains a formidable task owing to the presence of hardware-level errors and crosstalk. In recent years, machine learning (ML)-based methods have found widespread applications in different domains of science and technology for nontrivial tasks. In this work, we exploit the power of ML to design quantum gates that uses the hardware-level leakage errors to one's advantage. These gates are found to exhibit controlled leakage dynamics in and out of the computational states at appropriate times during the course of the gate that makes these extremely fast.