Training an open quantum classifier

Quantum machine learning (QML) aims to embed the power of quantum computation with learning theory. Quan-tum noise and finding the best recipe for encoding classical information into a quantum register could be seen as challenges to overcome for computational performance. Classification of quantum information is a subtask for QML. In this study, we adopt a dissipative route for quantum data classification and examine the developed theory on a gradient descent-based learning task. In particular, we follow repeated interactions based on open quantum dynamics where the binary decision is encoded on a steady state. Based on the analytical results, we develop a cost function for training an open quantum neuron. We demonstrate that the dissipation-driven protocol is suitable for a supervised learning scheme.

I. Introduction

Perceptron is a mathematical representation associated with biological neuron cells as basic units of neurocomputing. [1]–[3]. Artificial neural network (ANN)-based ML algorithms performed by the binary classification employed by percep-trons have numerous applications in the study of data anal-ysis [1], [2], [4]-[13]. According to Moore's law, regarding state-of-the-art semiconductors, the number of transistors in-creases linearly over time [14]. The issues with the production of chips in the nm scale reveal the fact that Moore's law has reached a physical limit.