Comparing Quantum Encoding Techniques
This work addresses the need for efficient quantum data encoding in applications like machine learning and quantum chemistry, but it is incremental as it compares existing methods without introducing new ones.
This study tackled the problem of comparing quantum encoding methods for hybrid quantum-classical machine learning by evaluating basis, amplitude, and rotation encodings using the QuClassi architecture on MNIST digit classification, resulting in metrics like accuracy, entropy, loss, noise resistance, resource usage, and computational complexity.
As quantum computers continue to become more capable, the possibilities of their applications increase. For example, quantum techniques are being integrated with classical neural networks to perform machine learning. In order to be used in this way, or for any other widespread use like quantum chemistry simulations or cryptographic applications, classical data must be converted into quantum states through quantum encoding. There are three fundamental encoding methods: basis, amplitude, and rotation, as well as several proposed combinations. This study explores the encoding methods, specifically in the context of hybrid quantum-classical machine learning. Using the QuClassi quantum neural network architecture to perform binary classification of the `3' and `6' digits from the MNIST datasets, this study obtains several metrics such as accuracy, entropy, loss, and resistance to noise, while considering resource usage and computational complexity to compare the three main encoding methods.