Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning
This work addresses the challenge of precise control in quantum computing hardware, which is crucial for advancing scalable quantum technologies, though it appears incremental as it applies existing machine learning methods to a specific domain problem.
The paper tackled the problem of designing high-fidelity multi-qubit gates for semiconductor quantum dot quantum processors by using deep reinforcement learning to optimize control pulses, achieving improved gate performance with specific fidelity metrics reported in the abstract.
In this paper, we present a machine learning framework to design high-fidelity multi-qubit gates for quantum processors based on quantum dots in silicon, with qubits encoded in the spin of single electrons. In this hardware architecture, the control landscape is vast and complex, so we use the deep reinforcement learning method to design optimal control pulses to achieve high fidelity multi-qubit gates. In our learning model, a simulator models the physical system of quantum dots and performs the time evolution of the system, and a deep neural network serves as the function approximator to learn the control policy. We evolve the Hamiltonian in the full state-space of the system, and enforce realistic constraints to ensure experimental feasibility.