论文标题
通过深度加固学习设计高保真多头门，以用于半导体量子点
Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning
论文作者
论文摘要
在本文中，我们提出了一个机器学习框架，以设计基于硅中的量子点的量子处理器的高保真多数门，并在单电子旋转中编码Qubits。在这种硬件体系结构中，控制景观是巨大而复杂的，因此我们使用深度强化学习方法来设计最佳的控制脉冲，以实现高富达多数的大门。在我们的学习模型中，模拟器对量子点的物理系统进行建模并执行系统的时间演变，而深度神经网络则是学习控制策略的函数近似器。我们在系统的整个状态空间中进化了哈密顿量，并强制执行现实的约束以确保实验性可行性。
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.
