论文标题
验证量子机学习中的公平性
Verifying Fairness in Quantum Machine Learning
论文作者
论文摘要
由于量子计算的超级经典能力，量子机学习是独立应用的，或嵌入了经典模型中以进行决策，尤其是在金融领域。公平和其他道德问题通常是决策的主要关注点之一。在这项工作中，我们为量子机器学习决策模型的公平验证和分析定义了一个正式的框架，在该模型中，我们根据直觉采用了文献中最流行的公平概念之一 - 任何两个类似的人都必须相似地对待，因此公正。我们表明，量子噪声可以提高公平性并开发出一种算法来检查（嘈杂的）量子机学习模型是否公平。特别是，该算法可以在检查过程中找到量子数据（编码个体）的偏置核。这些偏置内核产生了无限的许多偏置对，以研究模型的不公平性。我们的算法是基于高效的数据结构（张量网络）设计的，并在Google的TensorFlow量子上实现。 The utility and effectiveness of our algorithm are confirmed by the experimental results, including income prediction and credit scoring on real-world data, for a class of random (noisy) quantum decision models with 27 qubits ($2^{27}$-dimensional state space) tripling ($2^{18}$ times more than) that of the state-of-the-art algorithms for verifying quantum machine learning models.
Due to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a formal framework for the fairness verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notions of fairness in the literature based on the intuition -- any two similar individuals must be treated similarly and are thus unbiased. We show that quantum noise can improve fairness and develop an algorithm to check whether a (noisy) quantum machine learning model is fair. In particular, this algorithm can find bias kernels of quantum data (encoding individuals) during checking. These bias kernels generate infinitely many bias pairs for investigating the unfairness of the model. Our algorithm is designed based on a highly efficient data structure -- Tensor Networks -- and implemented on Google's TensorFlow Quantum. The utility and effectiveness of our algorithm are confirmed by the experimental results, including income prediction and credit scoring on real-world data, for a class of random (noisy) quantum decision models with 27 qubits ($2^{27}$-dimensional state space) tripling ($2^{18}$ times more than) that of the state-of-the-art algorithms for verifying quantum machine learning models.
