论文标题
在量子电路中查找破碎的大门---利用混合机器学习
Finding Broken Gates in Quantum Circuits---Exploiting Hybrid Machine Learning
论文作者
论文摘要
量子逻辑门的当前实现可能是高度错误的，并引入错误。为了纠正这些错误，有必要先识别故障门。我们展示了通过使用杂交量子和经典的k-nearest-neart-neymegnbors（KNN）机器学习技术在电路中发生栅极故障的过程。我们使用诊断电路和选定的输入量子位来完成此任务，以获得一组输出状态和参考状态之间的保真度。然后可以存储电路的结果，以用于经典的KNN算法。我们在数值上证明了在30多个门的电路中定位有故障的门，并且精度超过90％。
Current implementations of quantum logic gates can be highly faulty and introduce errors. In order to correct these errors, it is necessary to first identify the faulty gates. We demonstrate a procedure to diagnose where gate faults occur in a circuit by using a hybridized quantum-and-classical K-Nearest-Neighbors (KNN) machine-learning technique. We accomplish this task using a diagnostic circuit and selected input qubits to obtain the fidelity between a set of output states and reference states. The outcomes of the circuit can then be stored to be used for a classical KNN algorithm. We numerically demonstrate an ability to locate a faulty gate in circuits with over 30 gates and up to nine qubits with over 90% accuracy.
