  In this paper, we investigate the application of quantum and quantum-inspired
machine learning algorithms to stock return predictions. Specifically, we
evaluate the performance of quantum neural network, an algorithm suited for
noisy intermediate-scale quantum computers, and tensor network, a
quantum-inspired machine learning algorithm, against classical models such as
linear regression and neural networks. To evaluate their abilities, we
construct portfolios based on their predictions and measure investment
performances. The empirical study on the Japanese stock market shows the tensor
network model achieves superior performance compared to classical benchmark
models, including linear and neural network models. Though the quantum neural
network model attains a lowered risk-adjusted excess return than the classical
neural network models over the whole period, both the quantum neural network
and tensor network models have superior performances in the latest market
environment, which suggests the capability of the model's capturing
non-linearity between input features.
