Quantum Machine Learning: Practical Cases

Machine learning (ML) is often listed among the most potential applications for quantum computing. This is in reality a puzzling choice: Nowadays machine learning algorithms are extremely powerful in practice, but still theoretically challenging to understand. Quantum computing, in contrast, does not give actual benchmarks on practical scales. However, theory remains the main tool we have to determine if it may become important for a task. Although it is far from reaching its full potential, nevertheless, it presents several techniques to efficiently deal with the problem which are tough to solve traditionally. In this perspective we deploy the Quanvolutional Neural Networks (QNNs) on a quantum computer to recognize handwritten digits, and compare its performance with that of its classical counterpart namely the Convolutional Neural Networks (CNNs). We used a quanvolutional layer to separate the pixel data and demonstrate the advantage of applying QNN on a quantum computer. Finally, we implement the Variational Quantum Classifier (VQC) and several classical classifiers built on the Iris dataset, in order to compare their accuracies. We employed amplitude encoding to map data into the large Hilbert space of a quantum computer. The results revealed that the QNN model and VQC had high accuracy and low loss value compared to their purely classical counterparts.