论文标题
由信息库驱动的量子碰撞分类器
A quantum collisional classifier driven by information reservoir
论文作者
论文摘要
我们研究了与带有量子信息的不同量子量环境弱相互作用的探针量子量子的开放动力学。我们表明，根据耦合速率，提出的耗散模型在Bloch量子量参数空间中的稳态中产生了储层量子量量子的量子信息的二元分类。为了描述耗散模型动力学，我们采用了碰撞模型，其中储层量值的输入信息参数很容易确定。我们基于类似微剂的主方程的结果制定了广义分类规则，其中可以用Bloch参数描述分类。此外，我们表明，也可以通过量子参数估计来实现所提出的分类方案。最后，我们证明所提出的耗散分类方案适用于基于梯度下降的监督学习任务。
We investigate the open dynamics of a probe qubit weakly interacting with distinct qubit environments bearing quantum information. We show that the proposed dissipative model yields a binary classification of the reservoir qubits' quantum information in the steady state in the Bloch qubit parameter space, depending on the coupling rates. To describe the dissipation model dynamics, we have adopted the collision model, in which the input information parameters of the reservoir qubits are easily determined. We develop a generalized classification rule based on the results of the micromaser-like master equation where the classification can be described in terms of the Bloch parameters. Moreover, we show that the proposed classification scheme can also be achieved through quantum parameter estimation. Finally, we demonstrate that the proposed dissipative classification scheme is suitable for gradient descent based supervised learning tasks.
