Entanglement is a physical phenomenon, which has fueled recent successes of quantum algorithms. Although quantum neural networks (QNNs) have shown promising results in solving simple machine learning tasks recently, for the time being, the effect of entanglement in QNNs and the behavior of QNNs in binary pattern classification are still underexplored. In this work, we provide some theoretical insight into the properties of QNNs by presenting and analyzing a new form of invariance embedded in QNNs for both quantum binary classification and quantum representation learning, which we term negational symmetry. Given a quantum binary signal and its negational counterpart where a bitwise NOT operation is applied to each quantum bit of the binary signal, a QNN outputs the same logits. That is to say, QNNs cannot differentiate a quantum binary signal and its negational counterpart in a binary classification task. We further empirically evaluate the negational symmetry of QNNs in binary pattern classification tasks using Google's quantum computing framework. The theoretical and experimental results suggest that negational symmetry is a fundamental property of QNNs, which is not shared by classical models. Our findings also imply that negational symmetry is a double-edged sword in practical quantum applications.
翻译:虽然量子神经网络(QNNs)在解决简单的机器学习任务方面显示出令人乐观的结果,但目前,在QNNs中的纠缠效应和QNNs在二进制模式分类中的行为仍未得到充分探讨。在这项工作中,我们通过展示和分析量子二进制分类和量子代表学习的量子二进制分类和量子代表学习,从理论上对QNNs的属性进行了一些了解。我们用谷歌的量子计算框架来进一步评估二进制模式分类任务中QNNs的否定性对等。鉴于量二进制二进制信号的量子部分应用量子双进制信号,QNNS输出出相同的日志。也就是说,QNNS无法区分量制二进制信号及其在二进制分类任务中的否定性对应信号。我们进一步从经验角度评估了在使用谷歌的量子计算框架对量子表达式分类任务中的QNNSs的否定性相称性对等。理论和实验性结果表明,我们的理论和实验性模型也表明,我们的基本结果是两进制结果。