论文标题
用量子电路中二元分类中噪声的某些方面
Some aspects of noise in binary classification with quantum circuits
论文作者
论文摘要
我们正式研究了受实际量子硬件启发的受限制的单量噪声模型的影响，以及量子训练数据中的损坏，对使用量子电路进行二进制分类的性能。我们发现，在噪声模型中的假设下，即使在纠缠存在的情况下，量子的测量也只会受到该量子的噪音的影响。此外，当使用量子数据集拟合二进制分类器进行培训时，我们表明数据中的噪声可以作为正规器起作用，这意味着在某些情况下，在机器学习问题上噪声中的潜在益处。
We formally study the effects of a restricted single-qubit noise model inspired by real quantum hardware, and corruption in quantum training data, on the performance of binary classification using quantum circuits. We find that, under the assumptions made in our noise model, that the measurement of a qubit is affected only by the noises on that qubit even in the presence of entanglement. Furthermore, when fitting a binary classifier using a quantum dataset for training, we show that noise in the data can work as a regularizer, implying potential benefits from the noise in certain cases for machine learning problems.
