Continuing our analysis of quantum machine learning applied to our use-case of malware detection, we investigate the potential of quantum convolutional neural networks. More precisely, we propose a new architecture where data is uploaded all along the quantum circuit. This allows us to use more features from the data, hence giving to the algorithm more information, without having to increase the number of qubits that we use for the quantum circuit. This approach is motivated by the fact that we do not always have great amounts of data, and that quantum computers are currently restricted in their number of logical qubits.

翻译：延续我们对量子机器学习在恶意软件检测应用场景中的分析，本文探讨了量子卷积神经网络的潜力。具体而言，我们提出了一种新型架构，将数据沿量子电路逐层上传。这使得算法能够利用更多数据特征，从而获得更丰富的信息，同时无需增加量子电路中使用的量子比特数。该方法的动机源于：我们并非总是拥有海量数据，且当前量子计算机在逻辑量子比特数量上仍受到限制。