期刊
QUANTUM INFORMATION PROCESSING
卷 19, 期 8, 页码 -出版社
SPRINGER
DOI: 10.1007/s11128-020-02729-y
关键词
Quantum machine learning; Quantum computing; Quantum gates; Quantum algorithms
资金
- Air Force Office of Scientific Research
- Army Research Office
- National Science Foundation
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据