Cloning an unknown state is an important task in the field of quantum computation as it is one of the basic operations required in any experiment. The no-cloning theorem states that it is impossible to create an identical copy of an arbitrary unknown quantum state. Hence, techniques are developed to clone unknown states to high fidelities, rather than to exact copies. The usual method of cloning is quantum tomography, which measures a set of observables to reconstruct the unknown state. This method proves to be very expensive when the number of copies of the unknown state is limited. Here, we try to clone an unknown state in IBM’s QASM simulator using a quantum reinforcement learning protocol (Albarran-Arriagada et al. in Phys Rev A 98:042315, 2018), where the “right” amount of punishment/reward function and boundary conditions can give much better fidelity than what tomography can offer in limited copies of the state. Using this method, we can attain above 90% fidelity in under 50 copies. This method proves to be very useful for reconstructing quantum states when only limited copies of the state are available.

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在IBM Q体验平台上演示基于测量的自适应协议和量子增强学习

克隆未知状态是量子计算领域的重要任务，因为它是任何实验所需的基本操作之一。非克隆定理指出不可能创建任意未知量子态的相同副本。因此，开发了将未知状态克隆到高保真度而不是精确复制的技术。克隆的常用方法是量子层析成像，该技术可以测量一组可观察对象以重建未知状态。当未知状态的副本数受到限制时，此方法证明非常昂贵。在这里，我们尝试使用量子增强学习协议在IBM QASM仿真器中克隆未知状态（Albarran-Arriagada等人，在Phys Rev A 98：042315，2018）中，在这种情况下，“正确”的惩罚/奖励功能和边界条件可以提供比断层扫描在有限状态副本中所能提供的更好的保真度。使用这种方法，我们可以在不到50个副本中获得90％以上的保真度。当只有有限的状态副本可用时，该方法被证明对重建量子状态非常有用。