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Fast quantum gates are crucial not only for the contemporary era of noisyintermediate-scale quantum devices but also for the prospective development ofpractical fault-tolerant quantum computing systems. Leakage errors, which arisefrom data qubits jumping beyond the confines of the computational subspace, arethe main challenges in realizing non-adiabatically driven, fast gates. In thisletter, we propose and illustrate the usefulness of reinforcement learning (RL)to generate fast two-qubit gates in practical multi-level superconductingqubits. In particular, we show that the RL controller offers greateffectiveness in finding piecewise constant gate pulse sequences autonomouslythat act on two transmon data qubits coupled by a tunable coupler to generate acontrolled-Z (CZ) gate with 11 ns gate time and an error rate of $\sim 4\times10^{-3}$, making it about five times faster than state-of-the-artimplementations. Such gate pulse sequences exploit the leakage spacejudiciously by controlling the leakage dynamics into and out of thecomputational subspace at appropriate times during the gate application, makingit extremely fast.

Designing Fast Quantum Gates with Tunable Couplers: A Reinforcement Learning Approach