Robust high-fidelity three-qubit gates design for superconducting quantum computers via model-free reinforcement learning

Abstract

Multi-qubit gates are crucial to realizing quantum algorithms. Direct implementation of these gates will facilitate a reduction in circuit depth. However, existing model-based methods rely on precise characterization of the system Hamiltonian as well as execution of the control fields, which present challenges in obtaining high fidelity multi-qubit gates. In this paper, we exploit the bang-bang control protocol and model-free reinforcement learning to realize several kinds of three-qubit gates for superconducting quantum computers with tunable/fixed frequency qubits, including the iToffoli gate, the parity check gate and the CCZ gate. The simulation results show that the fidelity of each gate can achieve 99.2% with the Adam Gradient Descent Optimizer. Moreover, the proposed method is robust against random noise in the control pulse. Gate fidelity can be maintained over 95% even in the case of Gaussian noise with variance of 0.3. Besides, the impacts of parameter iterative optimization strategies are also analyzed.