The use of quantum machine learning and tensor network for Japanese stock return predictions

Abstract

With the development of quantum technology, the application of quantum computer to machine learning has gained attention. Tensor network, one of quantum-inspired algorithms has also been applied to machine learning and successfully solves various kinds of tasks. In this work, with the aim of evaluating their applicability for real world problems, we employ quantum neural network, one of quantum machine learning algorithms, and matrix product state, a well studied tensor network to predict Japanese stock returns. Based on their predictions, we further conduct the investment simulation and measure the performance, comparing with benchmarks, linear regression and classical neural network models. Our experimental result shows the matrix product state model outperforms other models. On the other hand, while performances of quantum and classical neural network models vary depending on the market conditions, the quantum model has the better performance than the classical one in the latest market environment.